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.

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[*] 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.

Taking advantage of loss aversion in education

Many years ago (I forget exactly when), I introduced extra credit into my ECON110 class (which is what is now ECONS102). The idea was to provide an incentive for students to attend class, since they could earn extra credit for completing various in-class exercises. A couple of years later, I briefly changed the way that I framed the extra credit, from being "extra marks that would be gained from attending", to "extra marks that would be lost by not attending".

If students were purely rational, the change from 'gain framing' to 'loss framing' the extra credit should have had no impact on student attendance. However, I was looking to exploit the fact that most people are quasi-rational, rather than purely rational. Quasi-rational decision-makers are loss averse, meaning that they value losses more than equivalent gains. For a loss averse person, losing $20 makes them unhappy to a greater extent than winning $20 makes them happy.

Does a change from 'gain framing' to 'loss framing' work? That is the question that this new article by Antal Ertl, Éva Holb (both Eötvös Lóránd Science University), and Barna Bakó (Corvinus University of Budapest), published in the Journal of Economic Behavior and Organization (open access), tries to answer. They use data from a field experiment at Corvinus University of Budapest, where students enrolled in a compulsory macroeconomics course for business students were randomised into one of three conditions: (1) Gain group, which earned points in each of four tests and the final examination as usual; (2) Loss group, which started each test and the final exam with full points, but had points deducted for each incorrect answer; and (3) Hybrid group, which was the same as the Gain group for the tests, but switched to the loss framing for the final examination.

Ertl et al. have a sample of 321 students who consented to be part of the research, completed an initial questionnaire at the start of the term, and earned a non-zero grade. Randomisation was conducted at the level of the tutorial group (so all students in a tutorial were in the same treatment), in such a way that each teacher had groups across more than one treatment. One wrinkle in their analysis is that the best three out of the four tests would count towards a student's grade, meaning that students may end up putting differential effort into each test, depending on how they have performed in the other tests already completed. So, in addition to looking at the effect of treatment on each test mark individually, Ertl et al. look at the effect on the 'best three' tests collectively, as well as the exam mark.

If randomisation were perfect and the treatment groups were balanced, the comparison between the Loss group and the Gain group would demonstrate the overall effect of loss framing on student performance. The comparison between the Loss group and the Hybrid group for the final exam, compared with the same comparison for the best three tests, would demonstrate whether students adjust in such a way that the loss framing has less impact over time (because the Hybrid group would be in their first loss-framed assessment, while the Loss group would be in their fifth such assessment). The treatment groups weren't perfectly balanced, with students sorting into tutorial groups in part based on whether they worked part-time. So, Ertl et al. control for working part-time, the tutorial day and time, and the tutorial group teacher, as well as other demographic and background variables.

In their main analysis, they find support for the positive effects of loss framing:

For the Loss treatment, the effect on the average of the Best 3 Tests is 3.2 percentage points, although the difference is not statistically significant. The treatment effect on the Final Test score, however, shows a large difference of 9.6 percentage points when not controlling for Best 3 Tests’ scores, i.e., how well students did throughout the semester before the Final Test.

After controlling for performance in the best three tests, the effect of the loss framing on performance in the final examination is a statistically significant 7.8 percentage points. Turning to the comparison of the Loss and Hybrid groups, Ertl et al. find that:

...the estimated effect sizes for Loss and Hybrid are essentially the same for the Final Test, once we take into account how well students did perform throughout the semester...

These results are consistent with loss framing leading to better student performance, and there being no novelty effect - the effect of loss framing doesn't appear to decline over time. Ertl et al. go on to show that the effects are similar for both male and female students, but larger for students who did not take advanced mathematics in high school than for those that did. They also show that the treatment did not seem to negatively affect students' perceptions of the course, because the teaching evaluations were similar for the different treatment groups.

Finally, Ertl et al. do provide a note of caution in their conclusion:

previous studies have highlighted possible psychological and motivational costs associated with loss framing... These findings suggest that the mechanism by which loss framing improves performance may, at least in part, operate through heightened tension and concern about avoiding mistakes rather than through enhanced intrinsic motivation. Moreover, in extreme cases, loss-framed grading may even produce adverse effects — for example, low-performing students might become discouraged early in the semester after ‘‘losing’’ too many points. Once it becomes apparent that only a passing grade is attainable at best, the loss-framed structure may make this limitation increasingly salient, potentially exacerbating anxiety and disengagement. Over time, this could have broader implications for students’ well-being and their willingness to enroll in courses or programs that employ such systems.

Ertl et al. don't directly test for these effects, but they should be a concern. We may be able to improve student performance through loss-framing assessments, but that might come at a cost to student mental health and wellbeing.

And that brings me back to the example I started with, from my ECON110 class. When I switched extra credit from gain-framed to loss-framed, student attendance in class did improve slightly. However, the bigger impact seemed to be the number of students who would contact me by email, seeking special consideration for missing the extra credit, offering to provide medical certificates or other evidence to explain their absence, and asking for extra chances to complete the in-class exercises. It turned out to be administratively much more costly for me, and so the change was short-lived (to the extent that I cannot even remember which year I tried this in). Those reactions could suggest a negative psychological effect of the switch from gain framing to loss framing.

So, not all interventions that are effective for promoting student performance should be adopted. We need to carefully consider both the benefits and the costs of the intervention first. Taking advantage of student loss aversion might be worth exploring further, but I would want to see a wider evaluation that included student wellbeing outcomes before adopting it.

This week in research #139

Here's what caught my eye in research over the past week (a quiet week, it seems):

• List (open access) comments on how to address the generalisability of research
• Lehner et al. (open access) find that the opening of a Walmart Supercenter is associated with a 2.2 percentage point (18%) increase in poverty, and that the increase is largest for younger and less-educated adults

And the latest paper from my own research (led by my former PhD student Muhammad Irfan, along with Ushan Goonawardane, and Craig Robertson), which was also covered in the New Zealand Herald:

• Our new article (open access if you register for free) in the New Zealand Medical Journal performs a comparison of methamphetamine contamination of 423 properties across New Zealand, before and after the requirement to test for methamphetamine was eased in May 2018, and finds a significant increase in methamphetamine contamination

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.

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[*] 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.