Minimum wages and the adoption of robots

Manufacturing firms typically have a choice of various production technologies. Some production technologies involve more labour. Others involve more automation (robots, as in this post). If labour becomes relatively more expensive compared with robots, firms have a greater incentive to adopt robots. That suggests that higher minimum wages, which make labour relatively more expensive for firms, may not only decrease employment (see the links at the end of this post for more on that point), but may increase the adoption of robots.

The extent to which firms adopt robots in the face of increasing minimum wages is the subject of this recent NBER Working Paper by Erik Brynjolfsson (Stanford University) and co-authors (ungated version here). They look at this question in two ways. First, Brynjolfsson et al. create a state-level measure of exposure to robots, which captures the extent to which robots are over- or under-adopted in each state, given the state's mix of industries and employment. They then correlate changes in that measure with changes in the state-level minimum wage over the period from 2003 to 2015. That correlation is illustrated in Figure 1(c) from the paper:

The regression line in the figure implies that a 10 percent increase in the minimum wage is associated with an increase in robot exposure equivalent to about 8 percent of the sample mean level of robot exposure.

Second, Brynjolfsson et al. use microdata from the US Census Bureau, including the Longitudinal Business Database and Longitudinal Firm Trade Transactions Database (LFTTD), to construct a panel dataset of robot adoption among US manufacturing firms from 1992 to 2021. They use the LFTTD data to identify which firms imported industrial robots. They then compare robot adoption between firms in adjacent counties on opposite sides of state borders, which face different state-level minimum wages but are likely to share many local economic conditions. Their measure of robot adoption in this analysis is simply whether a given firm adopted a robot in a given year, or not. In this second analysis, they find that:

...a 10% increase in minimum wage leads to an 8.4% rise in robot adoption relative to the sample average...

Notice how similar in magnitude the effects from their two analyses are, despite being quite different in nature, as well as covering different time periods. Both analyses suggest that higher minimum wages are associated with greater levels of robot adoption. The state-level relationship in the first analysis is clearly correlational, rather than causal. However, the comparison between firms in adjacent counties provides some plausibly causal effects (at least, there are plenty of other research papers that use a similar approach to estimate the causal effects of minimum wages). More generally, this research provides another example of how firms may respond to minimum wages on margins other than employment (see the links at the end of this post for more). When the relative price of labour increases, firms may change not only how many workers they employ, but also the production technology they use.

[HT: Marginal Revolution, back in February] 

Read more:

• A bunch of research findings on minimum wages in New Zealand
• The disemployment effects of Canadian minimum wages
• Jeffrey Clemens on the disemployment effects of the minimum wage
• The impact of an online minimum wage
• Seattle's minimum wage, revisited
• The latest evidence supports negative employment effects of the minimum wage
• Latest research suggests the minimum wage DOES reduce employment
• More empirical support for the disemployment effects of the minimum wage
• The minimum wage and job vacancies
• The minimum wage and hiring standards
• The employment effects of the minimum wage, from American Community Survey data
• The minimum wage and homelessness
• New York restaurants find a new way to respond to the minimum wage
• The minimum wage and workplace safety
• How employers respond to minimum wage increases
• Minimum wage increases and employer noncompliance
• Higher minimum wages and poverty revisited
• Minimum wages and health
• Effects of the minimum wage on the nonprofit sector
• 15 years of US research on the minimum wage elasticity of employment
• Local minimum wages and low-quality housing rents in Japan
• Minimum wages and student summer employment

Opportunity cost makes the news

Opportunity cost is one of the most underappreciated concepts in economics, and yet it is fundamental to good decision-making. Whenever we choose to use our resources for one thing, we give up what we could have done with them instead. The opportunity cost of something is the cost of foregoing the opportunity of using the resources for something else. More specifically, the opportunity cost is measured as the value of the next best alternative that is foregone.

Despite its importance, it is surprisingly rare to see opportunity cost mentioned in the media, even in business or economics stories. So, it was a delight to see this recent article in the New Zealand Herald:

Financial adviser Niran Iswar says almost every rental property he’s ever owned has lost money – and he thinks more people are coming around to the idea that it’s not always a surefire way to make money.

Iswar, who is head of accounting, wealth and advisory at Float, said once he counted the rates, insurance, maintenance and the opportunity cost of money tied up in rental properties, every rental he had held had gone backwards, except one that worked because it was bought at the right time “which is luck dressed up as skill”.

When making a decision about how to invest their savings, an investor has many alternatives to choose from. Each alternative comes with an opportunity cost - the return they could have earned from the best of the other alternative investments. The economic cost of an alternative includes all of the explicit costs, as well as the opportunity cost. In the case of rental properties, Iswar notes the rates, insurance, and maintenance, as well as the opportunity cost of the savings tied up in the investment.

Iswar is essentially saying that, once you take the opportunity cost into account, the cost of investing in rental properties (including the opportunity cost) exceeds the benefits. That is what he means when he says that the rental properties "have gone backwards". The savings would have been better off invested in some other alternative. As an example, an investor might be attracted to a rental property investment that offers an annual net return of six percent, but fail to consider that an alternative investment of comparable risk offers eight percent. The opportunity cost of the rental property investment is eight percent return foregone from the other investment. So although the rental property earns a positive net return of six percent, relative to the next-best alternative it actually generates an economic loss of two percent. By investing in the rental property, the investor would give up an eight-percent return in order to earn six percent.

Now, there is one important caution to note. In the context of financial investments, a straight comparison of returns ignores the role of risk. Different investments come with different risks, and different investors will have different appetites for risk. So, where investments differ substantially in risk, it is their expected returns adjusted for risk that should be compared. Only where the alternatives have broadly similar risks is a more straightforward comparison of returns appropriate.

Finally, while opportunity cost is not often mentioned explicitly, I imagine that many investors are implicitly taking it into account. Anyone who weighs up alternative uses of their savings and chooses the alternative that offers the best risk-adjusted return is already thinking in terms of opportunity cost. But making the opportunity cost explicit is useful, because it reminds us that simply earning a positive net return doesn't necessarily mean that an investment is a good one. We need to consider what else could have been done with the savings instead. That is why it was refreshing to see opportunity cost brought to the fore in the New Zealand Herald story.

Manufacturers' response to 'supercycles' in the market for computer memory

Back in May, David Oks wrote a fascinating Substack article about the economics of dynamic random access memory (DRAM) - the memory that is used in computers, smartphones, and gaming consoles, but also importantly in servers and data centres, such as those used to train generative AI models. I'm not going to focus on the effect of generative AI in this post, but instead on this particular part of Oks's article, which caught my attention:

And that combination—capital-intensive manufacturing plus fungibility—is a punishing combination. Because memory is fungible, the industry is intensely cyclical: the entire history of the DRAM industry is a history of boom-and-bust supercycles. First, strong demand from one sector or another—like Windows PC adoption in the 1990s—drives surging prices and a wave of investment from every player; cumulative overinvestment in an undifferentiated good produces oversupply; and then oversupply leads to collapsing prices.

And because production is so expensive, those down-cycles turn out to be existential: the memory industry is marked by constant wreckage. Intel dominated the memory game in the early 1970s but left in the 1980s, opting to focus on processors. Texas Instruments and IBM, also once major players, left in the 1990s. Germany’s Qimonda collapsed in 2009; Japan’s Elpida, once the world’s third-largest DRAM manufacturer, declared bankruptcy in 2012.

In my ECONS101 class, we teach a model of dynamic supply and demand that can be used to explain the boom-and-bust 'supercycles' that Oks describes. Consider the market for memory, and assume that it is perfectly competitive - most importantly, there are no barriers to entry into the market or barriers to exit from the market.[*] The market for memory is shown in the diagram on the left below. The diagram on the right will track changes in memory manufacturers' profits over time. Initially (at Time 0) the market is at equilibrium (where demand D0 meets supply S0) with price P0, and memory manufacturers are making profits π0. Now say there is a permanent increase in demand at Time 1, to D1. This increase in demand may be because of Windows PC adoption, or some other positive demand shock. Prices increase to P1, and memory manufacturers' profits also increase (to π1). There are no barriers to entry (this is a perfectly competitive market), so the higher profits encourage new manufacturers to enter this market (or more realistically, they encourage the existing manufacturers to increase capacity). However, new manufacturing capacity takes time to bring online, and firms make their investment decisions independently. By the time all of that new capacity becomes available, supply may have overshot the level required to simply meet the higher demand. Supply increases to S2 (more producers) at Time 2. Price falls to P2, and memory manufacturers' profits also fall (to π2).

Next, at Time 2 profits are low and some memory manufacturers will choose to exit the market (no barriers to exit because this is a perfectly competitive market), or more realistically it encourages the manufacturers to reduce their capacity. This helps explain the pattern that Oks describes, with Intel, Texas Instruments, IBM, and others exiting the market. Supply will decrease to S3 (fewer producers) at Time 3. Price will increase to P3, and memory manufacturers' profits will increase to π3. So, these 'supercycles' arise as memory manufacturers enter and exit the market in response to an initial increase in demand.

Now, memory manufacturers are not stupid. The manufacturers that remained after previous busts realised that they needed to change their approach in order to avoid these problems. Oks notes that:

And decades of collapse and consolidation left only a few players standing. In the 1990s, there were perhaps 20 meaningful producers of DRAM around the world; today there are three that account for more than 90 percent of global production. South Korea has two, SK Hynix and Samsung; and the United States has one, Micron.

And these memory makers have learned a very particular lesson from the unforgiving history of their industry: always leave demand unmet.

So, rather than rapidly increasing production in response to higher demand, the memory manufacturers have instead become much more cautious about adding capacity, deliberately allowing some demand to remain unmet. That reduces the risk that increasing supply causes a decline in prices and profits. However, the downside is, as Oks notes, that we now have a global shortage of memory as current production levels (and manufacturing capacity) are unable to keep up. That may open opportunities for new manufacturers to enter the market, even if they are less efficient (higher cost) than the incumbents. And therein lies the problem - by deliberately restricting supply tight, the incumbent manufacturers keep prices and profits high and that may eventually set off the next supercycle.

*****

[*] The market for computer memory isn't actually perfectly competitive, as there are likely to be large economies of scale in memory manufacturing. That means that a small firm entering the market would be at a large cost disadvantage to the large incumbent firms, and so small firms would be deterred from entering the market. However, there is another way of looking at this situation. Instead of considering new firms entering and exiting this market, think about the incumbent firms adding and subtracting additional manufacturing capacity. This has the same effect of increasing and decreasing supply, and leads to the same dynamic pattern in prices and profits.

Europe faces a flood of cocaine

The Financial Times reported back in July:

Cocaine production in Latin America has quadrupled in the past decade, with criminal networks in Colombia, Peru and Bolivia exploiting global trade routes to shift vast quantities of the drug. Cocaine is also increasingly shipped from Brazil into Europe via west Africa.

The UN warns that supply could soon exceed demand, increasing traffickers’ incentives to dump even more product on to Europe’s streets...

The result is that more cocaine is available in Europe today than in the 1980s — often seen as the drug’s heyday — according to the UN. Last year alone, its residue in city wastewater rose by more than a fifth, according to the EU’s drugs agency...

Lower prices and frictionless dealing on popular encrypted messaging apps have made cocaine more attainable.

My ECONS101 class covered the model of supply and demand last week, and this seems like a good example. The European market for cocaine is shown in the diagram below. The market was initially in equilibrium, where supply S0 meets demand D0. The equilibrium price of cocaine was P0, and the equilibrium quantity traded was Q0. The supply of cocaine has increased (a shift to the right, or down, of the supply curve for cocaine), to S1, due to increased production in Latin America being shipped to Europe. This lowers the equilibrium price of cocaine to P1, and increases the quantity traded to Q1.

However, the increase in cocaine supply hasn't had the same effect everywhere. The FT article also reports that:

One country where the retail price has risen rather than fallen, according to UN data, is the UK, which one former senior European police officer says may be linked either to stronger demand or to traffickers’ perception that it is riskier to smuggle cocaine into the country than elsewhere.

If cocaine traffickers believe that it is riskier to smuggle cocaine into the UK, that would decrease the supply of cocaine, and increase the price (see the diagram above, but with the change in supply reversed). That might also contribute to the increase in supply to Europe, if shipments that previously would have gone to the UK go to continental Europe instead. As for 'stronger demand', that effect is shown in the diagram below. The market was initially in equilibrium, where supply S0 meets demand D0. The equilibrium price of cocaine was P0, and the equilibrium quantity traded was Q0. If supply remained constant, and demand increased (as noted in the FT article), the demand curve shifts to the right (to D1). This increases the equilibrium price of cocaine to P1, and the quantity of cocaine traded to Q1.

The increase in demand could even lead to a price increase even with supply increasing as well. If the supply increased to S1, then the equilibrium price goes back to P0, with the quantity of cocaine traded increasing to Q1. 

Combining an increase in demand with an increase in supply is certain to lead to an increase in the equilibrium quantity. However, the change in the equilibrium price is ambiguous. Any smaller increase in supply than shown in the diagram would lead to a net increase in the price of cocaine in the UK, as would any decrease in supply. Any larger increase in supply would lead the equilibrium price to decrease.

Europe is, of course, not the only country dealing with an influx of cocaine. It has also been in the news in New Zealand recently as well. The underlying drivers are very similar, and are a recurring issue (see this post from 2017, related to the US cocaine market).

Read more:

• Cocaine makes a comeback in the U.S.

Will AI make personalised pricing a reality, and is that really a bad thing?

Personalised pricing (or first-degree price discrimination) occurs when the seller sells their good or service to every consumer at a different price, as I described in this 2023 post. If executed perfectly, the seller could extract all of the consumer surplus as profits, by charging a price to every consumer that is exactly equal to the maximum the consumer is willing to pay. Fortunately for consumers, such perfect personalised pricing has remained a theoretical possibility.

But technological tools are increasingly helping firms to learn more detailed information about consumer preferences, and that allows firms to home in on consumers' maximum willingness-to-pay. The latest worry for consumers is AI, as this article in The Conversation by Patrick Dodd and Hanoku Bathula (both University of Auckland) notes:

Digital platforms can observe thousands of individual decisions. A ride-hailing platform can see which jobs a driver accepts, when they work and which incentives bring them online. A retailer can see purchases, abandoned carts and responses to discounts.

There is no strong evidence major companies already know everyone’s precise financial breaking point. But algorithmically mediated pay, personalised worker incentives, discounts and consumer offers are already real.

Notice that Dodd and Bathula also take the logic of personalised pricing for consumers, and apply it to gig-economy workers as well. Platforms such as Uber or Lyft or Doordash can increasingly use what they know about their delivery workers' preferences to determine their minimum willingness-to-accept for each delivery. The target is different (minimising how much they pay to the delivery worker, rather than maximising the price they charge the consumer), but the underlying premise of personalised pricing is the same.

Algorithms have been around for a while, though. Dodd and Bathula do not clearly lay out why they think that recent developments in AI make personalised pricing more of a reality than before. Most of what they say about 'algorithms' applies equally to statistical algorithms that have been around for years (decades, even) as to more recent developments in AI and machine learning (AI/ML). So, let me extend their argument more explicitly.

AI/ML dramatically lowers the cost of estimating individual willingness-to-pay. It can combine huge numbers of relatively weak signals about a particular consumer, learn complex patterns from the behaviour of millions of other similar consumers, experiment continually with prices and discounts, and update its estimate each time that circumstances change. That gives firms far richer models from which to estimate each particular customer's maximum willingness-to-pay (or, for their workers, to estimate their minimum willingness-to-accept). Moreover, while older statistical algorithms allowed firms to segment customers into fairly coarse categories, AI/ML allows firms to make predictions for each individual, and in real time. The better estimates from these newer models therefore allow firms to price much closer to the perfectly price-discriminating ideal. It's still not completely perfect, but it is a further improvement on what they were previously able to achieve.

Dodd and Bathula finish their article by noting the unfairness of personalised pricing. Their argument is essentially that there is asymmetry in the relationship between consumers and firms. Firms using algorithms (and now AI/ML) know increasingly more about what consumers are willing to pay, but consumers know very little about what firms are willing to accept.

However, it is worth unpacking that a bit more. Firms that don't know consumer willingness-to-pay can't raise their prices without limit, as consumers with low willingness-to-pay would stop buying from them. In practice though, personalised pricing will never be perfect. Firms may charge higher prices to consumers that they estimate have high willingness-to-pay, while offering lower prices or discounts to consumers with lower willingness-to-pay. So, relative to offering the same price to everyone, personalised pricing need not make every consumer worse off. The high-willingness-to-pay consumers are likely to be worse off, but some low-willingness-to-pay consumers may actually be better off.

Now consider which types of consumers tend to have high willingness-to-pay, and which types tend to have low willingness-to-pay. For many goods, lower-income consumers are likely, on average, to have lower willingness-to-pay, so personalised pricing could result in some of them being offered lower prices. That won't always be true though. Some lower-income consumers with few alternatives or an urgent need may have high willingness-to-pay despite having a low income. Taken together, this means that the distributional effects of personalised pricing are not necessarily straightforward. However, in some instances preventing firms from price discriminating could be making low-income consumers worse off. With that in mind, is it really fairer that firms are not allowed to offer lower prices to consumers with low willingness-to-pay?

I'm not really trying to defend price discrimination here. I'm not keen on personalised pricing for very selfish reasons - I don't want to pay more, even if I am willing to pay more! And like me, most consumers should probably not be keen on personalised pricing. But before we rail against the evils of firms price discriminating, we need to properly consider its distributional consequences. And that means thinking about which groups may be made better off by price discrimination, not just which groups are made worse off.

Read more:

• Surge pricing is coming to a supermarket near you
• What would happen if perfect personalised pricing was possible?
• Personalised pricing is now a step closer with electronic shelf labels

The economics of pricing AFL Finals tickets

In my ECONS101 class, we have a topic that is devoted to understanding pricing and business strategies that deviate from the ideal 'marginal revenue equal to marginal cost' approach to pricing for firms with market power. In particular, I focus part of the topic on firms that price below the short-run profit-maximising price for strategic reasons.

One example is the NFL, which prices tickets for the Super Bowl too low. This is a surprising example to students, because the face value for the cheapest ticket for Super Bowl LX this year was $950. However, we know that this price is too low because the cheapest tickets on the secondary market were selling for around $6,400. That suggests that the NFL is leaving money on the table - they could earn much more if they set the ticket prices higher. Why would the NFL set the price lower than the short-run profit-maximising price for the Super Bowl? One reason may be that they want to maintain a long-term relationship with NFL fans. They want going to the Super Bowl to be an achievable aspiration for fans. Most fans won't be able to pay US$4,000 (plus the travel and accommodation costs) to attend every year, but at that price it is reasonable for fans to believe that they can attend once during their lifetime. If Super Bowl tickets cost over US$10,000, then that aspiration becomes much less achievable. This long-term strategy is also visible through the allocation of Super Bowl tickets. Each team gets an allocation of tickets, 35% of which must go to fans, and that allotment is typically given to the team's season ticket holders (see here).

The Super Bowl is recognisable to my students. However, thanks to this article in The Conversation by Paul Crosby (Macquarie University), I now have another example that is somewhat closer to home (albeit not necessarily more recognisable than the Super Bowl):

It’s AFL finals time – and in a season on track for record attendance, the league’s decision to freeze the price of entry-level finals tickets for an 11th straight year is smart economics...

So why are entry-level footy tickets staying cheap? It’s all about investing in the future – especially when there’s a lifetime of spending at stake...

Finals matches routinely sell out, so standard economics would suggest raising prices.

Instead, entry-level tickets for the AFL’s qualifying, elimination and semi finals, as well as this weekend’s wildcard round, have been frozen at A$35. Entry-level preliminary finals tickets will be $65, unchanged since 2016.

Crosby explains this pricing strategy as arising from fairness and goodwill towards fans, then notes that:

Cheap tickets can be an investment. A full stadium creates value beyond ticket revenue. Crowds generate the atmosphere that makes live sport attractive to television audiences and, in turn, valuable to broadcasters and sponsors.

An AFL supporter will often remain attached to the same club for decades, buying memberships and merchandise, watching broadcasts and eventually bringing their children along. A fan won over by a $35 ticket is worth far more than the profit on that ticket.

Keeping tickets affordable, and occasionally letting kids in free, helps recruit that next generation.

By keeping the price of finals tickets low, the AFL is foregoing short-run profit maximisation in favour of a long-term strategy that keeps fans engaged, keeps them attending games, and may lead to greater revenue and profits overall in the long run.

Crosby compares the AFL's pricing strategy with the approach adopted by music acts, who are increasingly using 'dynamic pricing' to maximise short-run profits from every concert. However, the situations are different in a meaningful way. The AFL can afford to have a long-term focus because it has a revolving cast of star players, while the teams endure. AFL fans typically follow a team, rather than particular players. In contrast, not every musical act is going to be The Rolling Stones, still touring after 60-plus years. For the most part, a musical act is not a revolving cast of musicians (especially for solo acts). A musical act's window for profiting from their talent is much shorter than the AFL, so we might expect to see short-run profit-maximising behaviour from musical acts than for the AFL. It is all about maximising profits over the appropriate time horizon. For the AFL or the NFL, a price that looks too low for today's ALF finals game or Super Bowl may be exactly the right price for maximising profits over the lifetime of a fan.

Computer gaming and binge drinking may be complements, not substitutes

In economics, two goods are substitutes if consumers tend to consume more of one if the price of the other increases. One way of thinking about that is that if the price of Good X increases, consumers switch to purchasing Good Y instead, and the quantity of Good Y demanded increases. Two goods are complements if consumers tend to consume less of one if the price of the other increases. In this case, if the price of Good X increases, consumers buy less of Good X (due to the Law of Demand), but also buy less of Good Y, and the quantity of Good Y demanded decreases.

Whether a pair of goods are substitutes or complements is determined by the cross-price elasticity of demand: the responsiveness of the quantity demanded of one good to a change in the price of the other good. If the cross-price elasticity is positive, the two goods are substitutes. If the cross-price elasticity is negative, the two goods are complements. Another way of thinking about this is that, following a change in the price of one good, ceteris paribus (holding all else constant), we would expect the quantities demanded of substitutes to move in opposite directions, while the quantities demanded of complements would move in the same direction.

There are obvious examples of substitutes and complements. Coke and Pepsi are the iconic example of substitute goods used in almost every introductory economics class. An example of complements that I use in my classes is video game consoles and games. However, it isn't always straightforward to determine whether a pair of goods are substitutes or complements. Sometimes they may be substitutes in one context, but complements in another. So, whether goods are substitutes or complements is an empirical question.

Take the example of computer gaming and binge drinking. When I was growing up, those two 'goods' certainly seemed like complements. My friends and I spent many nights drinking beer or RTDs and playing hotseat turn-based strategy games like Robosport, Warlords II, or Heroes of Might and Magic.[*] That experience made me a little surprised to see the hypothesis in this 2021 article by Torleif Halkjelsvik, Geir Brunborg, and Elin Bye (all Norwegian Institute of Public Health), published in the journal Drug and Alcohol Review (open access), which was that binge drinking and computer gaming are substitutes. Now, modern computer gaming differs in meaningful ways from how it looked when I was young. Nevertheless, I was surprised that Halkjelsvik et al. hypothesised in the direction they did.

Their hypothesis rested on several ideas, and was motivated by the observed increase in gaming and decrease in alcohol consumption by young people over time. First, alcohol and gaming are both outlets for thrill seeking, and are both responses to boredom, so increasing computer gaming might reduce the need for drinking. Second, both drinking and computer gaming are sources of social bonding, so again more computer gaming reduces the need for drinking.

Halkjelsvik et al. test their hypothesis with data from the European School Survey Project on Alcohol and Other Drugs (ESPAD), which surveys 15 and 16-year-old students every four years. They use data from 23 countries over the period from 1995 to 2015 (although noting that not all countries are part of the survey in every year), and look at the correlation between frequency of binge drinking (drinking five or more drinks on an occasion) and frequency of computer gaming, using a multi-level linear probability model. If their hypothesis that gaming displaces drinking is correct, the relationship should be negative. However, Halkjelsvik et al. find that:

...the association between country-level changes in computer gaming and binge drinking was estimated as positive...

So, increases in the average frequency of computer gaming at the country level tended to be associated with increases in the frequency of binge drinking. And, at the individual level:

The between individual-effect was positive, suggesting a four percentage point (±2 percentage points) higher binge drinking prevalence among students who report playing computer games daily.

Of course, the analysis that Halkjelsvik et al. conducted doesn't establish a causal relationship, it only shows correlations. And, importantly, they aren't directly testing whether computer gaming and binge drinking are complements in the economic sense, as that would require looking at how consumption of one responds to changes in the price of the other. However, their results are at least consistent with computer gaming and binge drinking being complements. Rather than moving in opposite directions, as we might expect if gaming displaced drinking (as Halkjelsvik et al. hypothesised), gaming and binge drinking tend to move in the same direction. Which, admittedly on the basis of a rather smaller and less representative sample, my friends and I could have told them.

*****

[*] My kids are bemused at the very idea that there was ever such a thing as hotseat multiplayer games. Sadly, they gradually died out as online games became more widely available in the early 2000s. However, they were really good for multi-tasking with some tabletop gaming at the same time, since only one player played the hotseat game at a time.

Customers shouldn't pay less when they use a self-checkout, they should pay more

The New Zealand Herald reported last week:

State representative Nikki Lucas has introduced a bill that would require retail businesses selling food in the state to offer a 10% discount to those who used the self-checkout lane.

“Retail businesses increasingly rely on self-checkout systems to reduce staffing and operational costs by shifting responsibilities traditionally performed by employees onto consumers,” she wrote...

Consumer NZ head of advocacy Gemma Rasmussen said her organisation thought there was validity to the argument in New Zealand, too.

Call me radical, but I think that Lucas and Rasmussen have this backwards. Customers shouldn't pay less when they use a self-checkout, they should pay more. To see why, I'm going to rely on the concept of price discrimination - where the seller sells the same good or service to different groups of consumers for different prices.

Consider two groups of consumers (impatient, and patient), and two options (self-checkout, and regular checkout). The first group of consumers is impatient, and they want to get out of the store as soon as possible, and for that reason they prefer to use self-checkout. This group can be said to have a short time horizon for their purchases. This short time horizon makes their demand for goods less elastic (less sensitive to price). The second group of consumers is more patient, and they are willing to wait. This group can be said to have a longer time horizon for their purchases, which makes their demand for goods more elastic (more sensitive to price).

If supermarkets want to price differently for each group, which group should pay the higher price? The answer to that question is shown in the two diagrams below. Both diagrams show a firm with market power (a supermarket), and each diagram corresponds to one of the sub-markets. The sub-market on the left represents the patient buyers, who have more elastic demand - notice that the demand curve D1 is relatively flat (which means that a change in price will have a big effect on the quantity that these consumers demand). The sub-market on the right represents the impatient buyers, who have less elastic demand - notice that the demand curve D2 is relatively steep (which means that the same change in price would have a smaller effect on the quantity that these consumers demand, than it would for the patient consumers). The marginal cost (MC) is the same in both sub-markets - it doesn't cost the supermarket any more to sell a product to an impatient buyer than what it costs them to sell that same product to a patient buyer. [*]

The supermarket will maximise profits by selling the quantity where marginal revenue (MR) is equal to marginal cost (MC) - this is the standard short-run profit-maximising condition (as I discussed in this post). In the impatient sub-market, the profit-maximising quantity occurs where MR2=MC, which is Q2. In order to sell that quantity in the impatient sub-market, the supermarket should set the price equal to P2. The problem with that high price P2 is that in the patient sub-market, no consumers would be willing to buy the good at all. The supermarket can increase profits if it charges a different price in the patient sub-market from the price it charges in the impatient sub-market. In the patient sub-market, the profit-maximising quantity occurs where MR1=MC, which is Q1. To sell that quantity in the patient sub-market, the supermarket should set the price equal to P1. In other words, the supermarket should charge a higher price to the impatient consumers, and a lower price to the patient consumers.

The problem here is that supermarkets don't know (for sure) which group (impatient or patient) any particular consumer belongs to. But by offering different checkout options, the customers can sort themselves into the impatient (less elastic demand) group and the patient (more elastic demand) group, because the impatient consumers use the self-checkout. In other words, the supermarket should charge a higher price to the users of the self-checkout.

This is an example of menu pricing (or second-degree price discrimination) - where the consumers are presented with a menu of options, and they select the one they prefer.  Crucially, the seller knows that some menu options appeal to consumers with more elastic demand, and other options appeal to consumers with less elastic demand. In this case, there are two menu options - self-checkout, or regular checkout, and the supermarket knows that the self-checkout appeals to the impatient consumers who should be charged a higher price.

So, customers who use a self-checkout right now shouldn't be arguing to lower prices. They should think themselves lucky that supermarkets aren't optimising, because if they were, the prices at self-checkouts would be higher than at regular checkouts.

*****

[*] You could argue that it doesn't cost the same to offer purchase through regular checkouts and self-checkouts. However, how big is the cost difference, really? Let's say that it takes two minutes to scan your items, but would take three minutes through the regular checkout, because the payment process tends to take a bit longer at a regular checkout. With self-checkout, the supermarket would save three minutes of labour. Say that the supermarket pays their checkout staff $30 per hour (somewhat more than the minimum wage). By using the self-checkout, you've saved the supermarket $1.50 of labour in this example (3/60 * $30). Except, that calculation doesn't take into account that the self-checkout is not a zero-labour option. There is usually a checkout person who has to watch over the consumers using the self-checkout. So, the saving is actually a bit less than that. It almost certainly isn't close to the 10 percent discount that Lucas is arguing for. Most of the cost of the items that you buy at the supermarket is the wholesale cost that the supermarkets pay, not the checkout labour cost.

The impact of using the CORE textbook in Uruguay

We introduced the CORE textbook The Economy at the University of Waikato when we recoded the compulsory economics paper in our management degree from ECON100 to ECONS101 (see here). We were early adopters, as the CORE textbook was only released in 2017. It was a big change, and largely a positive one. The CORE textbook was free, substantially lowering the cost for students to access an important learning resource. Because the textbook was online, it could be constantly updated. And I really liked the way that it turned the traditional approach to the teaching of microeconomics on its head. Instead of starting with perfect competition and the supply and demand model, and then teaching imperfect competition as an exception, the CORE textbook started with monopolistic competition (where firms sell products that are differentiated from those of their competitors) and teaches perfect competition as an exception. Since many firms operate in monopolistically competitive markets, the approach that CORE adopted seems more attuned to the real world that students see.

I've often wondered whether the CORE textbook improved students' learning though. So, I was interested to read this recent article by Federico Araya (Universidad de la República, Uruguay) and co-authors, published in the journal Economica (sorry, I don't see an ungated version online [*]). They evaluate the impact of adopting the CORE textbook for the introductory economics course at the Faculty of Economic Sciences and Administration (FCEA) at the Universidad de la República, the largest university in Uruguay.

Although the CORE textbook was introduced at FCEA in 2020, Araya et al. start their analysis from 2021, to avoid the impacts on online teaching during the pandemic. FCEA offered two introductory microeconomics courses, one of which used CORE and the other continued to use their traditional textbook. Students were randomly assigned to either course based on the last number of their identification document, with 30 percent of students assigned to the course that used CORE.

However, the textbook was not the only difference between the two courses. As Araya et al. explain:

Although attendance is optional in both courses, in 2022 and 2023, the CORE course introduced a modification to its evaluation system, assigning 10% of the total grade to group activities conducted during class sessions. This change may have created an incentive for higher attendance.

So, their evaluation is not a clean comparison of the same course taught with two different textbooks, but will compare two different pedagogies, one which uses the CORE textbook and in-class group activities that are worth grade points, and one that uses a traditional textbook without the in-class group activities.

Araya et al. then compare the two groups in terms of whether students passed the introductory microeconomics course, as well as whether students passed an introductory calculus course and whether they passed the intermediate microeconomics course that follows on from the introductory course, while controlling for a range of demographic and socioeconomic variables for each student. They find:

...no statistically significant differences in pass rates between CORE and the conventional course, with the exception of the 2022 cohort.

I was initially surprised that they decided to evaluate each cohort separately, rather than pooling them. However, the 2021 cohort is different because that year the CORE course didn't have the in-class group activities, whereas it did for the 2022 and 2023 cohorts. Combining the 2022 and 2023 results would give us a better sense of the overall effect (of the combined CORE textbook plus group activities intervention). Instead, we are shown a statistically significant positive effect in 2022, but no statistically significant effect in 2023. That doesn't tell us whether the effects in those two years were actually different from each other, or whether the combined intervention had a positive overall effect across the two cohorts. One further problem here that muddies the comparison is that the CORE and traditional courses didn't use the same assessment, and so passing one course may be different from passing the other. And that might also explain the different cohort-specific results (if the 2022 traditional course had more difficult assessments than the CORE course, for example).

That won't be a problem for comparisons in terms of student performance in introductory calculus and intermediate microeconomics. For those courses, Araya et al. also find no statistically significant effects on passing.

So, at least there is no evidence from this study that the CORE textbook (with or without in-class group activities) made students worse off. Although equally, there is no evidence that it made them better off either. That allows me to raise an issue that is general to much of the similar research on educational interventions (including my own research on the impact of AI tutors in ECONS101). We might expect to see no significant effect on student performance even from a successful intervention. That's because a successful intervention may make studying easier for students, freeing up time that they can then devote to other activities. That might be studying for their other courses (although notice that in this case any reallocation of study effort doesn't appear to have affected the probability of passing introductory calculus), or something entirely different (maybe working more, or having more leisure time). So, I'm not surprised to see no effect of CORE on student performance in this study.

We continue to use the CORE textbook in my ECONS101 class (although last year we moved to the new edition, The Economy 2.0). We don't follow the text very closely, at least not in the microeconomics section of the paper that I teach. Nevertheless, it continues to provide the base material for a lot of what we teach. And it's good to know that at least one study says that there is not evidence that continuing to use a 'non-traditional' text is doing harm to students.

*****

[*] It's kind of ironic that a paper evaluating the impact of an open-access teaching resource is not itself published open-access.

Read more:

• Goodbye to ECON100, hello to ECONS101
• Differing perspectives on introductory economics textbooks
• Does the CORE textbook not go far enough?

Is it worth starting a Division III college football programme?

College football starts towards the end of August. The big and successful college football programmes attract millions of dollars and hundreds, if not thousands, of additional student enrolments, as well as keeping alumni engaged. It's not just college students who care about the result of a Michigan vs. Michigan State matchup!

But does it pay off for colleges further down the NCAA ladder to have football programmes? In the NCAA divisional system, Division I contains the powerhouse athletic programmes, Division II consists mainly of smaller public and private schools, and Division III is reserved for schools that don't offer athletic scholarships. Is it worthwhile for those Division III schools to have a football programme?

That is the question addressed in this 2025 article by Bryan McCannon (Illinois Wesleyan University), published in the journal Economics of Education Review (ungated earlier version here). McCannon starts by noting the growth in the number of schools playing Division III football, as shown in Figure 1 from the paper:

I was surprised that so many of these schools have added football programmes over the last thirty years, but note that the increase has levelled off since the mid-2010s. McCannon looks at data from 1984 to 2021, for all schools that had a Division III programme in 2022. He is interested in whether there are changes in student enrolment, gender balance, and endowment. He employs a two-way fixed effects (TWFE) approach, which essentially compares changes at schools before and after they introduced Division III football with changes at schools that did not introduce it. Conventional TWFE estimates can be biased when schools adopt football at different times and its effects vary across schools or over time, so McCannon applies a correction for that problem and also uses a synthetic difference-in-differences approach.

It turns out that both approaches produce similar results, and those results are not favourable, and McCannon reports that:

I fail to provide evidence that the adoption of Division III football has any effect on undergraduate enrollment. The change is statistically indistinguishable from zero. In addition, I provide evidence that the proportion of the student body that is female reduces. Taken together, this suggests that any increases in the male student population attributed to the introduction of football is offset by either reduced demand from female students or changes in the institution’s admissions practices.

And in terms of endowments:

Schools which added college football were overdrawing their endowments prior to adoption... I fail to find evidence that the addition of college football reverses these downward slides. This suggests that it did not sufficiently energize alumni giving or reduce financial pressure on the institutions.

So, adding a Division III football programme appears to offer little measurable reward for a college or university, at least in terms of the outcomes McCannon looked at. Why then would these schools start football programmes? One possibility is that they are caught in a competitive arms race, which is a type of prisoners' dilemma (which I covered in my ECONS101 class this week). A Division III college may believe that introducing football will attract students away from rival institutions, or prevent it from losing students when other Division III colleges introduce football. But if every Division III college introduces a football programme, none of them gains relative to the others, but they all incur the cost of running a football programme. The result is that many Division III colleges introduce football programmes, only to leave them all worse off (or at least no better off, based on McCannon's results).

Alternatively, McCannon suggests in his conclusion that it may be attractive for these schools to add a football programme in times of financial distress, in order to attempt to reverse the decline. However, these results suggest that such efforts would be largely unsuccessful in reversing declining enrolments or financial pressure.

These Division III schools are typically small, and education-focused. Perhaps they should stick to their strengths, and leave the expensive football programmes to the larger schools?

Can financial incentives help heavy drinkers stay sober?

Rational (and quasi-rational) decision-makers respond to incentives. If the costs of doing something go up, they tend to do less of it. If the costs go down, they tend to do more. And the reverse is true of benefits. Changing the costs and/or benefits of an activity therefore should be expected to change behaviour.

Does that logic extend as far as behaviours involving addiction and self-control problems? Consider alcohol consumption. Can heavy drinkers be incentivised to remain sober, at least temporarily, by increasing the costs of drinking, or increasing the benefits of not drinking? That is essentially the question addressed in this 2019 article by Frank Schilbach (MIT), published in the prestigious journal American Economic Review (open access).

Schilbach conducted a field experiment over three weeks with 229 cycle-rickshaw drivers in Chennai, India. In the experiment, the drivers were randomly split into three groups. The first group received a financial incentive to remain sober (the 'Incentive group'). The second group were paid an unconditional payment of similar magnitude (the 'Control group'). The third group got to choose between the sobriety incentives and the unconditional payment (the 'Choice group'). To receive their payment, the study participants had to report to the study office and submit to a breathalyser test. Schilbach was really interested in the effect of alcohol consumption on savings behaviour, so each research participant was offered the opportunity to save money at the study office each day. He was also interested in the effects on labour market participation and earnings, which were determined using surveys of the research participants.

The results reveal a number of important things about rational behaviour among heavy drinkers. First, the group that was given the choice between sobriety incentives and an unconditional payment demonstrated a strong demand for sobriety:

One-third to one-half of study participants chose sobriety incentives over unconditional payments, even when this choice entailed a potential or certain reduction in study payments...

One-third of the participants in the 'choice group' were willing to give up as much as 30 percent of their study earnings in order to be given the sobriety incentives. Schilbach isn't able to definitively determine why there was such high demand for sobriety, but he does note that:

First, study participants had significant experience with alcohol consumption and the potentially resulting self-control problems. The average study participant had been drinking alcohol for over a decade and many of them had been drinking (almost) daily...

Second, individuals perceived the costs associated with their drinking as significant. Many individuals expressed a strong desire to reduce their drinking in surveys and informal conversations. These men had spent substantial income shares on daily alcohol consumption for many years before participating in the study. Compared to these expenses, the forgone study payments due to the commitment choices may have appeared relatively small to individuals, especially if they implied a positive (perceived) chance of reducing subsequent alcohol consumption in the longer run.

So, the research participants may have perceived the experimental setting, and the money on offer, as a way to commit themselves to sobriety, at least for the period of the study. Did the incentives work, though? Schilbach finds that they did:

In the pre-incentive period, about one-half of the individuals in each of the three groups visited the study office sober. This fraction gradually declined in the Control Group to about 35 percent by the end of the study... In contrast, with the start of the incentivized period, sobriety in the Incentive and Choice Groups increased by about 10 to 15 percentage points. Subsequent sobriety at the study office also declined in these two groups, but the difference to the Control Group remained roughly constant.

Regression models confirm that the Incentive and Choice groups were approximately 13 percentage points more likely to visit the study office sober than the Control group, and the average breath alcohol content (BAC) was 2 to 3 percent lower for the Incentive and Choice groups than for the Control group (conditional on visiting the study office). Schilbach notes that the effect was largest on daytime drinking and not overall alcohol consumption, suggesting that many study participants simply shifted their drinking to later in the day (after visiting the study office).

Did sobriety affect labour market outcomes? Schilbach finds small and statistically insignificant effects on labour supply, hours worked, and earnings. As for savings, Schilbach found that the intervention increased savings, with the Incentive and Choice groups saving about 50 percent more than the Control group over the study period. Schilbach interprets this as showing that:

...increasing sobriety reduced self-control problems in savings decisions. An alternative interpretation could be that alcohol is a key temptation good for this population such that reducing alcohol consumption mitigates the need for commitment savings. However, given that the intervention only moderately reduced overall alcohol consumption and expenditures, this channel is unlikely.

My takeaway from this paper is that many heavy drinkers recognised their own self-control problems and were willing to give up some income for a commitment device that would help them remain sober. The commitment device increased the costs of drinking (or, equivalently, increased the benefits of not drinking). So, the drinkers who chose the sobriety incentives were acting rationally in response to a change in incentives. The research participants who shifted their drinking to later in the day were also acting quite rationally. By shifting their drinking to later in the day, they could receive the benefits of the sobriety incentive, while continuing to drink (albeit later in the day). In other words, the incentive changed behaviour, just not necessarily in the way it was intended to.

So, if you wanted to roll out a broader intervention based on changing incentives for heavy drinking, it might be better to measure sobriety at multiple times of the day. However, in this context even the later drinking may have reduced some of the potential alcohol-related harm, since there may have been fewer drunk-driving cycle-rickshaw drivers on the streets of Chennai (although, to be fair, the study doesn't actually show that there was less drink-driving).

It would be interesting to know how much of these study results are context-dependent, and whether a similar intervention would work elsewhere. If you tried to incentivise heavy drinkers in a high-income country to reduce their consumption, would they respond in a similar way? That question will have to wait for future research.

Egg prices will rise in New Zealand, even without a major avian flu outbreak

Last year, I posted about avian flu in the US and the impact on egg prices, noting that prices will rise. Thankfully there hasn't been a major outbreak of avian flu in New Zealand as yet, although it seems likely there will be soon. Domestic birds, such as chickens, are at risk, and as I noted in that earlier post, that affects the supply of eggs. And New Zealand egg suppliers are acting now, as the New Zealand Herald reported earlier this week:

It comes as New Zealand’s largest egg supplier Mainland Poultry, accounting for nearly 40% of the country’s eggs, is putting hundreds of thousands of free-range chickens into lockdown after the deadly bird flu virus was detected in the country last week.

Putting free-range chickens into lockdown will raise the costs of production for free-range eggs. The effect on the market for free-range eggs is shown in the diagram below. Before the chickens were locked down, the free-range egg market was in equilibrium, where demand D0 meets supply S0, with a price of P0 and a quantity of free-range eggs traded of Q0. The lockdown increases the costs of producing free-range eggs, which decreases supply to S1. This increases the equilibrium price of free-range eggs to P1, and reduces the quantity of free-range eggs traded to Q1.

Free-range eggs and colony eggs are substitutes. Once free-range eggs become relatively more expensive, some consumers will switch to colony eggs. The effect on the colony eggs market is shown in the diagram below. Before the change in the price of free-range eggs, the market for colony eggs was in equilibrium, where demand DA meets supply SA. The equilibrium price was PA, and the quantity of colony eggs traded was QA. Since some consumers switch to the relatively cheaper colony eggs, that increases the demand for colony eggs from DA to DB, increasing the equilibrium price of colony eggs from PA to PB, and increasing the quantity of colony eggs traded from QA to QB.

Overall, eggs are going to cost more, regardless of whether they are free-range eggs or colony eggs. And even without a major outbreak of avian flu. If avian flu does take hold in New Zealand, the price of eggs of both varieties will go up even further.

Are men's and women's soccer complements or substitutes?

Both my ECONS101 and ECONS102 classes touched on the subject of complementary and substitute goods this week (in different model contexts). Two goods are complements if consumers tend to consume them together. In that case, a decrease in the price of one good would increase the quantity that the consumer buys of both goods. Two goods are substitutes if consumers tend to consume one or the other. In that case, a decrease in the price of one good would increase the quantity that the consumer buys of the now-cheaper good, but decrease the quantity that the consumer buys of the other good (which is now relatively more expensive).

Often, it is easy to tell if goods are complements or substitutes. However, sometimes it is not straightforward. Consider the example of men's and women's soccer matches. Are they complements, or substitutes? If, when faced with the choice of whether to attend a men's or a women's soccer match, or both, fans tend to choose one or the other (and not both), then the matches are substitutes. On the other hand, if fans tend to go to both, then the matches are complements. Another way of thinking about this is that, when the price of one of the matches goes up, what happens to attendance at the other. So, if the ticket price for a men's soccer match increases and attendance at women's matches goes up, then they are substitutes, whereas if attendance at women's matches goes down, then they are complements.

Ultimately, whether men's and women's soccer are substitutes or complements is an empirical question. Fortunately, this 2025 article by Galila Nasser and Christian Deutscher (both Bielefeld University), published in the Journal of Sports Economics (open access), provides us with an answer. Or rather, they provide us with an answer in one particular context, which is German soccer.

Specifically, Nasser and Deutscher use data from the 2009/10 to 2018/19 seasons of the Frauen-Bundesliga, and look at the impact on match attendance when a Frauen-Bundesliga match is played on the same day as a men's Bundesliga match. They also consider whether the effect is larger when the overlapping men’s and women’s matches involve teams belonging to the same club. Their dataset contains 1,256 Frauen-Bundesliga matches, including 851 played on the same day as a men's Bundesliga match and 118 played on the same day as a match involving the men's team of the same club.

Controlling for the day of the week, week of the season, the weather, whether a UEFA Champions League match was also being played that day, and a variety of variables capturing the popularity of the match, Nasser and Deutscher find that there is:

...an approximately 15 percentage points decrease in attendance when women’s games coincide with men’s games on the same day.

A minor quibble with the paper is that when they say a 15 percentage points decrease, they really mean a 15 percent decrease. And the effect for matches played by the same club on the same day is somewhat larger, with attendance lower by about 16 percent. So, these results are consistent with men's and women's top-league soccer matches in Germany being substitutes (fans tend to go to men's or women's games, and not both). However, we can't conclude this for certain as the results are based on observational data so they are correlations, not causal. Nevertheless, Nasser and Deutscher conclude that:

For matches on the weekend, it is essential for clubs that have both men’s and women’s soccer teams in the first Bundesliga to avoid scheduling their matches on the same day.

Given that the seasons overlap substantially, and clubs in both leagues understandably want weekend matches, another option might be to make joint attendance at both men's and women's matches more attractive. Clubs with both men’s and women’s teams could offer a combined ticket covering matches played on different days, or even arrange occasional double-headers. As I note in my ECONS101 class, this sort of bundling can be an effective pricing strategy when there is heterogeneous demand across multiple products. Provided the variation in fans' willingness to pay for the ticket to the combined event is lower than the variation in fans' willingness to pay for the tickets separately, then bundling has the potential to increase total revenue overall. And that higher total revenue can then be shared between the men's and women's teams. Whether that would work here is another empirical question. Perhaps Bundesliga clubs could indulge us by running the experiment?

Farmers can't avoid high synthetic nitrogen fertiliser prices by switching to organic fertiliser

The New Zealand Herald reported yesterday:

New Zealand farmers face hefty increases in the price of fertiliser this spring as a result of the escalating US-Iran conflict and the war in Ukraine.

The Middle East plays a big role in the global fertiliser market because of its supply of natural gas and mineral resources.

Russia is also a major supplier of fertiliser.

Renewed hostilities in the Persian Gulf – and the virtual closure of the Strait of Hormuz – have driven oil prices up to about US$90 ($154) a barrel for Brent crude, the international benchmark.

Synthetic nitrogen fertiliser is generally manufactured from ammonia created using the Haber-Bosch process. This requires hydrogen, which is often derived from natural gas (mainly methane). Since the Middle East is a major supplier of natural gas, a lot of nitrogen fertiliser is manufactured in the Middle East. The current conflict in the Middle East is constraining the transport of nitrogen fertiliser from the Persian Gulf, reducing the supply of nitrogen fertiliser.

The effect of this on the market for nitrogen fertiliser is shown in the diagram below. The market was initially in equilibrium, where demand D0 meets supply S0, with a price of P0 and a quantity of nitrogen fertiliser traded of Q0. The Middle East conflict reduces shipping of nitrogen fertiliser, which decreases supply to S1. This increases the equilibrium price of nitrogen fertiliser to P1, and reduces the quantity of nitrogen fertiliser traded to Q1.

Can farmers avoid the higher price of nitrogen fertiliser by switching to an alternative product, such as organic fertiliser (compost, or manure)? Not really. Consider what happens in the market for organic fertiliser, shown in the diagram below. Before the change in the price of nitrogen fertiliser, the market for organic fertiliser was in equilibrium, where demand DA meets supply SA. The equilibrium price was PA, and the quantity of organic fertiliser traded was QA. Since nitrogen fertiliser and organic fertiliser are substitutes, and nitrogen fertiliser is now relatively more expensive (as shown above), farmers switch to the relatively cheaper organic fertiliser. That increases the demand for organic fertiliser from DA to DB, increasing the equilibrium price of organic fertiliser from PA to PB, and increasing the quantity of organic fertiliser traded from QA to QB.

So, the effect overall is that the price of both nitrogen fertiliser and organic fertiliser increase. Farmers cannot easily avoid high fertiliser prices. We can expect that to flow through into higher prices for farm produce, as well as lower profits for farmers.

Why rising honey prices may increase kiwifruit orchard costs

This week, my ECONS102 class covered rational behaviour, one aspect of which is the cost-benefit principle: that when evaluating mutually exclusive alternatives, a rational decision-maker will choose the alternative that offers the greatest net benefit (the greatest difference between benefits and costs). So, it was interesting to see a good example of this in The New Zealand Herald just last month:

There’s growing competition for beehives as honey prices sweeten again and kiwifruit orchards continue to grow...

[Beekeeper Liam Gavin] said renewed confidence in honey production is seeing some pivot away from pollination.

“I sort of describe it as the tug of war between honey and pollination.

“Both are needing more beehives. So which one, where are they going to go? And that’ll all be down to, like, region-specific [stuff], and what people like to do in terms of how they beekeep.”...

With honey prices coming back up, [New Zealand Kiwifruit Growers Incorporated chief executive Colin] Bond expected more beekeepers would prioritise honey over pollination, which would create a challenge for kiwifruit growers.

Beekeepers can position their hives primarily to generate income from honey production, or primarily to generate income by providing pollination services. Thus, for a particular hive at a particular time, honey production and paid pollination are mutually exclusive alternatives.

A rational beekeeper, applying the cost-benefit principle, would compare the expected net benefit from using their hives for pollination with the expected net benefit from using them for honey production. That comparison would include pollination fees, expected honey revenue, transport and feeding costs, risks to hive health, and other relevant costs and benefits. As honey prices increase, the opportunity cost of committing hives to pollination increases. Ceteris paribus (holding all else constant), as honey prices increase fewer hives will be offered for pollination.

So, if kiwifruit growers (and other farm and orchard businesses that depend on pollination) want to secure enough hives for pollination, they will probably need to offer higher pollination fees. That would raise their pollination costs and, consequently, their overall orchard operating costs.

Spark's new overseas roaming charges and price discrimination

I've just gotten back home from three weeks in Europe. One irksome but necessary aspect of travelling is mobile phone roaming. While I was away, Spark introduced new roaming charges, and their new options both increase the price per day of roaming for most overseas trips, and price discriminate so that those staying overseas for longer pay a higher price for roaming. As the New Zealand Herald reported:

Spark customers travelling overseas for the school holidays face new charges to stay connected, with the telco scrapping its cheapest $25 fortnightly roaming pack.

The company is overhauling its roaming plans, with the new charges taking effect this Friday, including an automatic $10-a-day fee if customers don’t turn roaming off...

Previously, pay monthly customers could use 2GB of data on one of the provider’s 14-day roaming packs, priced at $25 and $30...

Three new packs will replace the old plans, alongside a new daily roaming option.

A $30 14-day pack will still be available to prepaid customers.

The other options will provide travellers with 20GB to use over 30 days, a change the company believes will make roaming simpler and more predictable.

“This helps our customers to stay connected for longer, with fewer top-ups, less uncertainty, and greater confidence about what they’ll pay.”

While the $50 and $65 packs have a higher upfront cost, customers would receive five times more data to use than under the previous plans, the spokesperson said.

If you look at the price per gigabyte of data, the new roaming packs are clearly much better value. Customers are paying twice the price, but getting ten times the data. So, high data users are likely to be better off under these plans. I want to focus instead on travellers who are not using large amounts of data (and for simplicity, I'm going to focus on the data-only packs, not the more expensive packs that include roaming calls and texts). For those travellers, when you look at the cost per day of roaming, the new packs are far more expensive. This is illustrated in the diagram below, which shows the costs for up to 30 days of roaming. The bold green line shows the existing pricing for a 14-day data-only roaming pack ($25 for each 14-day period). The light blue dashed line shows the cost using the new $10 daily roaming rate. The orange dashed line shows the cost for the new 30-day data-only roaming pack ($50 for each 30-day period).

For a Spark customer roaming for one or two days only, the new daily roaming pack is the cheapest option. So, if you're travelling to Australia for a day or two of shopping or to attend a concert or a sporting event, the new option is a better deal than what was previously on offer. With the new options, daily roaming is lower cost than buying a 30-day pack for up to four days of roaming, and the same cost as the 30-day pack for five days of roaming. Beyond that, you would be better off buying the 30-day roaming pack, even if you are only roaming for seven days.

The comparison between the old 14-day roaming pack and the 30-day roaming pack makes it clear that anyone roaming between five days and 14 days will now be paying twice as much as before. From 15 to 28 days, the cost of roaming with the new packs is the same as for the old packs. For someone like me, who typically goes overseas for a conference and might be away for 10-14 days at a time, this is clearly going to increase the cost of roaming.

It may be that Spark has determined that the new pricing options better reflect actual customer usage. That is what a Spark spokesperson argues in the New Zealand Herald article. However, it is also clearly an example of price discrimination in action. Travellers going overseas for a few days likely have more elastic demand for roaming than travellers going overseas for a longer time. That's because of the availability of close substitutes. If you go overseas for a few days, you could make use of free hotel and airport WiFi, or be prepared to just switch off mobile data for the time you are away, rather than paying for roaming. So, travellers who go overseas for a few days are likely to be relatively price sensitive. Travellers going overseas for a longer time are less likely to be able to switch off mobile data for that length of time, making them less price sensitive. The optimal pricing therefore is to set a higher price for travellers going overseas for a longer time than for those going overseas for a few days.

Price discrimination is very common in practice. In this case, Spark is using price discrimination and that will likely increase their profits. And that means that many travellers who are not high data users, myself included, will be paying more for roaming in the future.

Turkish inflation drives consumers to incur extreme shoe-leather costs

Inflation imposes costs on people. One of the costs of inflation is that it gives people strong incentives to spend time and effort avoiding higher prices. They can do that by reducing their cash holdings, searching harder for low prices, or, in extreme cases, travelling to shop elsewhere. When inflation is high, and prices are increasing rapidly, consumers have a strong incentive to spend a lot of time doing these things. Economists call these shoe-leather costs, because when consumers have to walk around a lot of stores in order to compare prices, their shoes wear out. At least, that's a literal explanation of the term. In an age where prices are published online, the actual act of 'walking around to compare prices' is a lot easier on the shoes. Or is it? An extreme example has been playing out recently, as reported in Bloomberg last November (paywalled, but you can find an ungated version here):

Almost every month, Cihan Citak gets into his car, passport in hand, and sets off from Istanbul to Alexandroupolis, a Greek seaside city 40 kilometers (25 miles) from the Turkish border. After a roughly four-hour drive, he walks the crowded aisles of the local supermarket, filling his cart with wine, cheese and other groceries that cost a fraction of what they do back home...

Cross-border retail has become routine for many who found that Turkey’s surging food prices and stronger lira make Greece a cheaper alternative for everyday purchases. The trend, while not new, is accelerating: 6% of all Turks crossing the border to Greece in the first nine months of the year were on a shopping run, the highest share of overall travelers since at least 2012, data from the country’s statistics agency show.

When inflation causes people to drive four hours in order to find lower prices, you know the shoe-leather costs must be high. The inflation rate in Türkiye is over 30 percent. That isn't hyper-inflation, but it is very high. For comparison in New Zealand, the inflation rate spiked at about 7 percent just after the pandemic, but that was the highest it had been in over 30 years. Inflation more recently has been between 2.5 and 3.5 percent, which is higher than the Reserve Bank's mandate to keep inflation between one and three percent in the medium to long term.

All of that is to say that Türkiye’s much higher inflation creates much stronger incentives for consumers to incur shoe-leather costs to avoid higher prices than is currently the case in New Zealand

[HT: New Zealand Herald, also paywalled]

My 18-month detour through second-degree price discrimination terminology

When I was composing this post about price discrimination last month, I was drawn into a discussion with ChatGPT about second-degree price discrimination. ChatGPT, which I mostly use for checking for inconsistencies and grammatical errors in my draft blog posts, told me that I should refer to menu pricing as a form of second-degree price discrimination. I replied that wasn't correct, because second-degree price discrimination, as defined by Arthur Pigou in the early 1920s, involves offering a declining price for each additional unit that the consumer buys. ChatGPT responded that indeed, Pigou had defined second-degree price discrimination that way, but that in much current industrial organisation usage, second-degree price discrimination includes cases where consumers are offered different options and sort themselves into groups that have different price elasticities of demand (or different willingness to pay) for the good.

That discussion made clear that I had been on an 18-month detour in how I described the degrees of price discrimination. Only last year, I changed the definitions of the degrees of price discrimination in my ECONS101 class to match those that Pigou uses, and therefore moved menu pricing into the definition of third-degree price discrimination (or group pricing). I've held off on posting about my exchange with ChatGPT until now, because I didn't want to confuse my students in this trimester's class about what did, and did not, fall under the different degrees of price discrimination before they were tested on it (and, as it turns out, I didn't test them on that specific aspect of the topic in any case). [*]

This appears to be one of those situations where terminology changes meaning over time, and is a cautionary lesson in making sudden changes to definitions on the basis of reading about the history of economic thought. The issue here is that I had come across Pigou's definitions in one source, and initially dismissed it as it was inconsistent with the way we taught that topic. But then I read The Economics Book by Niall Kishtainy and co-authors (which I reviewed here), which made me more certain about Pigou's definitions. To be clear, I'm not blaming Kishtainy et al. They were perfectly correct in terms of Pigou's definitions. I should have checked some other sources for more current usage. One example is the excellent book Information Rules, by Carl Shapiro and Hal Varian (which I read in 2023 and reviewed here), which made the definitions used in industrial organisation clear (although Shapiro and Varian preferred to use the term 'versioning', rather than second-degree price discrimination).

Now I'm left with the task of combing through my past posts, to ensure that I update my terminology, or revert it to the original text in the few cases where I went back and made changes. I don't want to risk confusing future students, which is a risk given that I refer them to my posts for further detail and examples on topics that we discuss in class.

*****

[*] I didn't perfectly achieve this goal, because one very alert student picked up the error through her own conversations with Harriet, our ECONS101 AI tutor, an irony that was not lost on me.