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.

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.

*****

[*] 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.

Generative AI, cognitive offloading, and escaping the 'illusion of competence'

Last week, for maybe the first time, I found myself telling a student not to use AI. That might sound extraordinary. After all, generative AI hit the big time with the release of ChatGPT in November 2022, and increasing numbers of students have been using it ever since. Many lecturers immediately freaked out, and many institutions initially reacted by banning or restricting AI use, before realising that they were fighting a losing battle against the incoming tide of generative AI and trying to impose 'guardrails'.

I've never asked my students not to use generative AI. In fact, I've encouraged it. I even have custom AI tutors set up for each of the papers I teach, that use a knowledge base of materials from the paper to give students targeted assistance. I have little to fear from generative AI, because the vast majority of assessment in my papers is in-person and invigilated (that's one of the beautiful things about teaching first-year papers - I can argue that basic concepts and applications can be authentically assessed in an exam environment).

Anyway, back to the story. The student was using our class AI tutor during class, to give them a solution to a problem we were working on during class. I pointed out that it defeated the purpose of doing the problem in class, if they used Jane (our ECONS102 AI tutor is named after Jane Marcet, the author of the 19th-Century popular economics book, Conversations on Political Economy) to solve it for them. The problem wasn't the use of Jane per se (after all, I encourage them to use her). It was that by using Jane to solve the problem for them, they were missing out on a key learning opportunity.

Probably, I was a little hard on the student. After all, they were using the tools available to them, and engaging in cognitive offloading - reducing the demand or mental load that they face by offloading a task onto generative AI. And increasingly, students are engaging in this cognitive offloading, sometimes in helpful ways, but often in ways that are detrimental. That is one of the conclusions from this 2026 report (with non-technical summary on The Conversation) by Jason Lodge and Leslie Loble (both University of Technology Sydney).

The report has a lot of quotable quotes. For instance, they note that:

It is not possible to engage in critical thinking when one has nothing to think critically about. A person does not simply think critically in a vacuum. A scientist thinks critically about a flawed methodology by drawing on a vast store of knowledge about experimental design. A historian thinks critically about a primary source by drawing on their knowledge of the document’s social, political, and historical context...

This, to me, highlights the key challenge that education faces with generative AI. In order for students to be well prepared for engaging with generative AI, they need to be able to evaluate AI output. And without a thorough grounding in disciplinary knowledge, their evaluations would at best be superficial. And that is why I hold the line on having assessment in my papers that explicitly excludes the use of generative AI. My papers build the foundation on which students' later use of generative AI, and their evaluation of AI outputs, can build.

Lodge and Loble note that:

Every task or learning activity is essentially now a group activity. It just so happens that the other member or members of the group are machines that have practically all human knowledge at their fingertips (in their databases/algorithmic weights). Like any other group activity, students can benefit from that collaboration or get the smart kid to do all the work for them.

That is absolutely what is happening. Students' learning activities are now mostly group activities, even when they are the only human in their group. In group work, how the work is shared is important, and that is where cognitive offloading comes in. Lodge and Loble distinguish between two forms of offloading:

• Beneficial offloading occurs when AI is used to manage extraneous cognitive load (e.g., checking grammar), freeing a learner’s limited working memory to focus on essential, intrinsic tasks.

• Detrimental offloading (outsourcing) occurs when a learner uses AI to bypass this intrinsic cognitive effort (the desirable difficulties) required to build long-term knowledge schemas. This offloading also seems to extend to vital metacognitive and self-regulated learning capabilities, compounding the negative impact of outsourcing on learning.

Importantly, Lodge and Loble note that students typically don't understand metacognition. They haven't intentionally engaged in thinking about their own thinking and understanding how they learn or managing that process. In my experience, many students tend to have been passive recipients of learning approaches, without really engaging with the process themselves. And even those that do engage usually haven't thought deeply about how they learn. And so, when they use a tool that gives them ready answers, it may seem to students that they are learning more efficiently. Lodge and Loble label this an 'illusion of competence', noting that:

Research has long shown that fluent learning materials, such as high-quality videos, can lead people to greatly overestimate how much they have learned by mistaking the ease of processing (fluency) for the depth of learning...

Lodge and Loble's report doesn't stop at the point of diagnosing the problem though. They present three main solutions, that involve:

• shifting generative AI use towards beneficial offloading, where students free up cognitive resources to focus on intrinsic learning. Lodge and Loble offer the example that "AI can be used to provide scaffolding, structured practice, and feedback, all aimed at managing the cognitive burden on the learner and enabling progressive independence...";
• deliberately designing AI interactions to include metacognitive responsibilities, so that students must pause, reflect, and assess their own understanding; or
• shifting the fundamental role of AI from being an 'answer oracle' to a tool that provokes intrinsic load. Lodge and Loble offer examples such as asking students to teach the AI (which plays the role of a confused student), setting up AI as a Socratic tutor, or asking students to independently verify AI outputs.

Those solutions have implications for how I design and use AI tutors in my papers. In order to limit students from engaging in detrimental cognitive offloading, the AI tutor shouldn't simply act as an answer machine. Instead, they should encourage students to attempt problems themselves, offer hints or scaffolding when they get stuck, and ask them to explain or justify their reasoning. And, importantly, the AI tutor could also prompt students to reflect on what they understand, what they don't understand, and whether they could solve the problem on their own. The master prompt for my AI tutors does instruct them to take a Socratic approach, but they don't adhere to it strictly. I'll certainly be putting more thought into the master prompt to see if I can dissuade them from being answer machines and to incorporate more of the metacognitive elements in the future.

The solutions provided by Lodge and Loble are useful, and hopefully they prompt other lecturers to think intentionally about students' (and possibly their own) engagement with generative AI. I especially like the second option, because I believe that we all (and not just students) can benefit from better understanding our learning process, and recognising when we are engaged in genuine and effortful learning. This is not the first time I've encountered concerns about cognitive offloading in the context of generative AI and education (see here). And it is interesting that the same, or a similar, set of solutions keep being presented. In particular, integrating technology as a complement, rather than a substitute, for thinking is important. Generative AI has dramatically lowered the cost of getting answers. It hasn't lowered the cost of learning. A better understanding of metacognition is therefore important too.

Perhaps, then, the lesson from my interaction with the student isn't that they shouldn't have been using generative AI in class. It's that they need to understand when using generative AI in class supports their learning, and when it substitutes for the thinking that learning requires.

I think most universities are now considering explicitly including generative AI in the curriculum, in order to better prepare students for future careers that will no doubt involve substantial interactions with generative AI. Perhaps we should also be considering explicitly including metacognition in the curriculum?

Read more:

• Neuroscientific insights into learning and pedagogy, especially in the age of generative AI

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?

How important is the apprenticeship model to entering a research career?

Those of us working in research careers can invariably share stories about working as a research assistant, cleaning datasets, coding, running models, doing literature reviews, and many of the other less-glamorous tasks that make up the research process. That was a key aspect of our apprenticeship into the world of research, and a gateway into a research career. But how important really is research assistance as an entry point into a research career?

That is the question addressed in this 2025 NBER Working Paper (ungated version here) by Ina Ganguli (University of Massachusetts, Amherst) and Raviv Murciano-Goroff (Boston University). They look at the impact of working in a university lab (an important subset of research assistance work) on subsequently pursuing a scientific career. Interestingly, Ganguli and Murciano-Goroff use changes in the local minimum wage as an exogenous source of variation in lab employment, so this paper also indirectly contributes to the literature on the employment effects of the minimum wage, in a context (research labs) that is not often the focus of that literature.

Their data comes from UMETRICS, which collates data on research grants across universities, and their dataset covers 32 universities over the period from 2000 to 2019 (although the dataset has coverage up to 2022, including those additional years would mean having to account for the COVID-19 pandemic).

First, using a dataset collated at the lab level, Ganguli and Murciano-Goroff use a staggered difference-in-differences approach to look at the impact of minimum wage changes on employment of undergraduates in the labs. This analysis essentially compares the change in undergraduate employment between the time before and the time after an increase in the minimum wage, between university labs that were affected by the minimum wage increase and those that were not. In that analysis, they find that:

...following minimum wage increases, labs decrease the employment of undergraduates by 7.4% on average...

So, not dissimilar to the literature on minimum wage effects on employment, when focused on young people in exposed occupations. Ganguli and Murciano-Goroff then use the minimum wage change as an instrument for students' exposure to laboratory research as an undergraduate. The key assumption is that minimum wages while an undergraduate affect later scientific careers only through their effect on lab employment opportunities.

Using a dataset of over 28,000 undergraduates and their subsequent career paths, Ganguli and Murciano-Goroff find that:

...decreased exposure to scientific work translates into significantly lower rates of undergraduate research assistants pursuing doctoral degrees or working in the life sciences sector after graduation. We find that working one fewer quarter in a lab during an undergrad student’s college years translates into between a 7.0 and 10.3 percentage point decrease in the rate of enrolling in a doctoral-level program. Given our sample of 28,283 students, this implies that if all students had experienced a minimum wage increase, roughly 500 fewer undergraduates in our sample would have pursued these advanced degrees.

So, the results imply that one fewer quarter of lab experience as an undergraduate reduces enrolment in a doctoral programme by 7.0 to 10.3 percentage points. That is a fairly large effect and should make graduate research programmes take notice of the importance of undergraduate research assistance opportunities for the pipeline into graduate research.

So, working in a lab as an undergraduate is a key pathway towards a career in the life sciences, and when those opportunities are restricted, students are less likely to embark on such a career. These results won't be too surprising to those of us who have been through an apprenticeship as a researcher. It is likely that I would be doing something very different right now if I hadn't been pulled into a lot of research projects towards the end of my undergraduate studies. I almost certainly wouldn't have pursued a PhD, or become an academic. Research assistant jobs are more than just jobs. They provide mentoring, information, skills, networks, references, and a chance to try out being a researcher in a safe setting.

These results also highlight two other things for me. First, they suggest that minimum wage increases may have a negative impact on the career pipeline into science. I'm unsure that this negative impact of the minimum wage has been identified before. However, we should be cautious because Ganguli and Murciano-Goroff are primarily interested in using minimum wage changes to identify the effect of undergraduate research experience on later careers, rather than estimating the overall long-run consequences of minimum wage increases for the scientific workforce. Nevertheless, their results suggest that this may be an important unintended consequence of higher minimum wages, and one that would be worth further research.

Second, given that research assistants do a lot of the 'drudge work' in research, if generative AI is also able to do a lot of that work, that further suggests a negative impact on the career pipeline into science through the rise of generative AI. I know others have written on this before (see here), so the challenge here is not a new idea.

On the plus side, that suggests another method that could be used to further test for the impacts of undergraduate (and graduate) research assistance on future academic careers, beyond focusing on life sciences (as Ganguli and Murciano-Goroff do). By comparing academic fields that are more (or less) exposed to generative AI (especially in the early days of generative AI), we might be able to tease out how important research assistance is to future academic careers across many fields.

Research apprenticeship is important, and these results give us a clear indication of how important it can be. Cleaning data, running models, searching the literature, and doing all the other seemingly mundane tasks of a research assistant are not just cheap ways for senior researchers to get research done. They are also how the next generation of researchers learns what research is, discovers whether they enjoy doing it, and gets started on a research career. If these opportunities are reduced, whether through higher minimum wages or through substitution by generative AI, we may save on some of the drudge work today, but at the cost of having fewer researchers tomorrow.

[HT: Marginal Revolution]

This week in research #138

Here's what caught my eye in research over the past week:

• Akter et al. argue that New Zealand’s low-risk drinking advice is outdated and now overdue for review
• Jones (open access) outlines two scenarios for the impact of artificial intelligence on the economy, providing some guidance about the potential future consequences of AI
• Lee and Porter (open access) provide a simple introduction to the 'weak instruments' problem, including some practical do’s and don’ts implied by the findings of the weak instruments research literature
• Poterba and Werning (open access) summarise the work of last year's John Bates Clark medal winner, Stefanie Stantcheva

Book review: Humble Pi

Alongside my professional interest in popular economics books, I'm a sucker for popular mathematics books like Jordan Ellenberg's How Not to Be Wrong (which I reviewed here). And I particularly like these books if they are fun. So, I was really looking forward to reading Humble Pi, by Matt Parker.

And I wasn't disappointed. Humble Pi is equal parts sombre (after all, there are plenty of maths errors that have led to tragic outcomes, such as the Challenger space shuttle) and amusing (such as Sun Microsystems employee Steve Null, whose details kept disappearing from their database because his last name was NULL). Parker says that the book:

...is a collection of my favourite mathematical mistakes of all time. Mistakes aren't just amusing... they're revealing.

And he's right. There is a lot to learn from this book, while at the same time being amused by (at least some) of the mistakes. Who knew that there was a word 'frigorific'? (a frigorific mixture is a combination of chemicals that always stabilises to the same temperature). And Parker writes in both an engaging and funny style. Consider this:

Do biologists use Excel to process their data? Is the phosphoglycan C-terminal? Yes! (Well, I think it is. It was that or 'Do BP1FB1 genes secrete in the woods?!' but I wasn't confident about that one either. I'm way beyond my limit of biological knowledge trying to look up even obvious microbiology things.) Look: the point is yes. Cell biologists use Excel a lot.

I laughed, until I stopped. And then I laughed again. And there was lots of that while I read this book. Parker also has some pet peeves that come through in the book, such as a strong aversion to stars shining through the shadowed part of a crescent moon. On that point he even takes aim at the Sesame Street classic, I Don't Want to Live on the Moon.

I'll let him off for the anti-Sesame-Street rant, as the rest of the book is a lot of fun to read. I even found the page numbers starting high and counting down to zero to be quite endearing (actually, if I'm honest, I wish more books numbered their pages that way). If you're looking for a good diversion or a change of pace, and don't mind learning a thing or two about maths along the way, this might be a good book for you. Recommended!

Can a simple email nudge students to seek study help?

Students don't always access the help that they need. Often, it's because they don't realise what support is available to them. I'm interested in any way that we can make it easier for students to access support that enhances their learning and helps them to succeed. So, I was really interested to read this 2020 article by Rita Balaban and Patrick Conway (both University of North Carolina at Chapel Hill), published in the AEA Papers and Proceedings (ungated earlier version here).

Balaban and Conway evaluate the impact of emails (passive nudges) reminding students about two economics support programmes that were available on the UNC Chapel Hill campus. The two programmes were the 'EconAid Center', which provided walk-in peer tutoring during weekdays and early evenings, and a series of five hour-long learning strategy seminars run by the co-authors. Students were randomly assigned to one of two email groups, one of which received weekly reminders about the EconAid Center, while the other received reminders before each learning strategy seminar. The evaluation focuses on the 199 students who consented to participate.

Those emails represent a very light-touch intervention. But did they work? The nudges did get students to engage with the programmes, but only for female students and first-year students, who were more likely to engage with the EconAid Center if they received the email nudges. Female students and students of colour were actually less likely to attend the learning strategy seminars if they received the email nudges. So, at best a mixed result.

Turning to whether the two programmes worked to improve grades:

The coefficients corresponding to the effects of the support programs on performance are positive but insignificantly different from zero.

Now, the small sample of 199 students (and the much smaller samples for subgroups) may simply have been too small to estimate the effects precisely. Participation in the programmes was also relatively low. A larger study might therefore do a better job of telling us whether the programmes actually had an effect or not.

So, file this study away under possibilities for the future. Light-touch nudges may help some students take the first step towards seeking support. A natural extension today would be to test whether similar nudges can steer students towards effective human or AI-assisted study support.

Why the City Rail Link is already showing up in property prices

This past week, my ECONS102 class covered hedonic demand theory, which suggests that when you buy certain goods (like houses, cars, computers, or land), you are really buying a bundle of characteristics, and each of those characteristics individually has value. So, when you buy a house, you are really buying a bundle that includes a number of bedrooms, bathrooms, car parking, views, and access to local amenities. And when those characteristics change, then the value of the house will change.

So, it should be no surprise then that the City Rail Link (CRL) will change property prices, since access to good transport links is a valued characteristic. And buyers are taking notice. The exact opening date of the CRL has not yet been announced, although it has been expected in late August or early September. And yet, in anticipation of higher land values in the future, the demand for land around stations that will benefit from the CRL has increased now. As the National Business Review reported back in June (paywalled):

On the residential front, and after reviewing Real Estate Institute data, CBRE found that while Auckland residential prices have risen 29% since 2016, prices around station catchments – defined as a 10-minute walk (about 800 metres) to catch a train – have climbed by an average 36%.

The top-performing areas were Morningside, Kingsland and Baldwin Avenue in Mt Albert, which came in at 108%, 95% and 84% respectively, despite a similar increase in supply during that time...

If you correctly anticipated that the price of an asset would increase in the near future, and you bought it now, you would reap a 'windfall gain'. However, by buying the asset now, you are increasing demand for that asset. And if lots of others also anticipate higher future prices and decide to buy now, that increased demand (and competition for the asset) will tend to drive prices up now. That theory is consistent with the observed increase in property prices, which the CBRE report notes is concentrated around stations that will benefit from the CRL and not other suburbs.

A combination of anticipatory demand and hedonic demand has pushed up land (and house) prices. Hedonic demand explains why improved transport links are valuable, while the anticipatory demand effect explains why that value can appear before the first CRL train even runs.

This week in research #137

Here's what caught my eye in research over the past week:

• Avina et al. (open access) conduct a meta-analysis of studies into public attitudes toward immigrants in host societies, finding that while preferences are broadly similar across countries and demographic groups, economic considerations have become more influential over time, and evaluations of individual immigrants differ sharply depending on where people stand on the broader immigration debate
• Márquez and Scartascini (open access) evaluated a randomised controlled trial of a behavioural economics course for public officials in Latin America and the Caribbean, finding that the course improves applied reasoning and problem-solving
• De Boer et al. (open access) compare trust behaviour towards AI (ChatGPT 4o) and human receivers using a trust game in an experimental setting, finding no significant differences in trust behaviour either between individuals and groups or between AI and human receivers
• Voorintholt et al. (open access) find that reported climate change worries are on average significantly higher when people are asked whether they 'worry about climate change', compared with when they are asked if they agree that 'climate change 'worries me' (survey wording matters!)
• Morris (open access) challenges whether the concept of 'alcohol use disorder' is confused
• Anagol, Ferreira, and Rexer (with ungated earlier version here) estimate the economic value of zoning reform in São Paulo, finding that the reform increased the aggregate housing stock by 1.6 percent, reduced house prices by 0.4 percent, and produced welfare gains of 0.65 percent of city GDP
• Murphy (with ungated earlier version here) evaluates a randomised control trial in rural Kenya in which selected households received cognitive behavioural therapy and medication to reduce alcohol abuse, finding that the program decreased the likelihood of positive spot breathalyser tests among men by 14 percentage points, the likelihood of heavy drinking by 19 percentage points, and increased household real annual harvest values by 32 percent

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.

Could student-run social media groups reduce university student dropout?

Universities are quite focused on student retention, and as I noted in this 2018 post, if we can identify at-risk students, perhaps we can help to find ways to ensure they succeed. However, around that time I had a summer research scholarship student looking into the reasons that students drop out, and it turned out that each student dropped out for quite different, and difficult to predict, reasons. I call this the Anna Karenina principle of dropout: 'all students who persist are alike; each student who drops out does so in their own way'.

What if there were a simpler way of reducing student dropout, that did not require universities to identify at-risk students in advance, but instead reduced the risk of dropout from the outset? That would seem to be an attractive proposition.

So, I was interested to read this 2021 article by Lucio Masserini (University of Pisa) and Matilde Bini (European University of Rome), published in the journal Socio-Economic Planning Sciences (ungated version here), which evaluates the impact of student-created social media groups, such as Facebook pages, on student dropout. Masserini and Bini use survey data from 1879 first-year students from a major university in Central Italy.

Why would joining social media groups reduce dropout? Masserini and Bini suggest that these groups may help students form social connections and feel greater 'belonging' within the university community, while also providing a way for students to share information about courses, assessments, and study materials.

The key challenge in the analysis is that students are not randomly assigned to join social media groups - they choose whether or not to do so. And students who join these groups may differ from those who don't in ways that would bias a simple comparison of the students who joined social media groups and those who didn't. For example, more engaged students, who are less likely to drop out, might also be more inclined to join university-related social media groups run by other students. Masserini and Bini deal with this using propensity score matching - which involves identifying 'control' students who didn't join a social media group but who are most similar to each 'treated' student who did join a social media group. Then, comparing their matched control and treated students deals with any observable differences between the students who did, and did not, join social media groups.

Masserini and Bini then report a range of results of the estimated impact of social media groups on dropout, based on different assumptions used to do the matching, and:

...with the exception of k=1 nearest-neighbour, all the estimates indicated that students joining groups or Facebook pages had, on average, a lower probability to dropout, compared with those who were not part of such groups. The results also showed that the extent of the difference between the treated and control groups was not negligible, as it varied from 0.081 to 0.113, depending on the matching algorithm.

So, the results suggest that joining student-run social media groups or Facebook pages reduces the probability of a student dropping out by between 8.1 and 11.3 percentage points. Now, I should note that I don't in general find propensity score matching to be terribly convincing as a way of dealing with selection bias. 

Now, I should note that I do not find propensity-score matching entirely convincing as a way of dealing with selection bias. Although matching can make the treatment and control groups similar on observed characteristics, there is still something that is different about the treated and control students that leads the treated students to choose to join social media groups and the control students to choose not to join. That something is an omitted variable in the propensity score matching approach, and it is unclear how big the omitted variable bias will be. If, for example, joiners are more motivated or feel more connected to university life, then some of the apparent effect of joining the group on the probability of dropping out may instead reflect those underlying differences. Masserini and Bini's results are robust across several matching methods and sensitivity checks, which is reassuring, but robustness checks cannot establish that there isn't some omitted variable bias in the matching.

Having said that, if we take these results at face value, then there may be some merit in having student-run social media groups that university students can join. We must bear in mind that these results come from a survey in 2016, and they may not have aged well. But social media groups still exist, and students still participate in them. It could be worth exploring whether these effects still hold, given that increasing student retention remains a key focus for universities.

Having said that, student-run social media groups would probably be a relatively inexpensive way for universities to reduce dropout. Now, these results come from students surveyed in 2016, and both social-media use and the university environment have changed considerably since then. Nevertheless, the basic idea remains plausible. Universities could support the creation of student-run groups and randomly encourage or 'nudge' some students to join, then compare their subsequent retention with that of students who were not encouraged. That experimental approach would provide more contemporary and causal evidence of whether the groups reduce dropout, rather than merely attracting students who were already less likely to drop out.

Read more:

• Predicting student success, and failure
• Big data, student dropouts, and failing fast

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.

This week in research #136

Here's what caught my eye in research over the past week:

• Baker et al. (with ungated earlier version here) find that following legalisation, sports betting does not displace other gambling or consumption but significantly reduces savings, as risky bets crowd out positive expected value investments, and that these effects are concentrated among frequent bettors and low-savings households
• Alfani (open access) summarises the state of research on social mobility and income inequality in preindustrial societies, and identifies several promising avenues for new research
• Bostwick and Nguyen (with ungated earlier version here) find that when a student enrols in a triweekly (rather than biweekly) university class, they earn lower grades and are less likely to take a subsequent course in that same field
• Kaffine and Rao (with ungated earlier version here) find that the observed distribution of satellite orbits is well explained by profit-maximising satellite operator behaviour, and that collision risk plays a key deterrent role
• Yang et al. (open access) find that having daughters slightly increases the preference for gender equality and homosexual rights, using data across 39 countries from the European Social Survey
• Di Tella, Gàlvez, and Schargrodsky (with ungated earlier version here) find, using a laboratory experiment, that two strategies often proposed to reduce political polarisation on social media, removing access to the platform and exposing users to counter-attitudinal content, do not reduce polarisation and may instead produce mild increases
• Perroni et al. (open access) find, using a sample of 40,000 job vacancies in Vietnam, that physically attractive women are offered higher salaries, whereas physically attractive men are not
• Sentana and Gómez-Bengoechea comparing the behaviour of a groups of professional soccer players and inexperienced college students when they play the same two-person zero-sum game in both a soccer stadium (the field) and a video game (the lab), and find that while both groups behave consistently with the implications of mixed strategy equilibrium in the field, their behaviour rejects the theory in the lab

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.

How career stereotypes shape students' choice of major

What jobs do accounting majors get? How about psychology majors? Or economics majors? If you answered, respectively, 'accountant', 'psychologist', and 'economist', you're probably far from alone. When most people think about particular fields of study, they have stereotypical jobs in mind, and they're far more likely to believe that majors get the stereotypical job than any other job. Even in the case of economics, where very few graduates will go into a job with the title 'economist' (many will go into a job with some sort of 'analyst' title, like a business analyst, market analyst, or financial analyst, etc.).

A new article by John Conlon (Ohio State University) and Dev Patel (Brown University), published in the Quarterly Journal of Economics (open access) demonstrates the extent of this stereotyping. They also show using a simple survey experiment that students' stereotypical views can be changed, affecting their choice of major.

The first part of the paper compares students’ beliefs about the careers associated with different majors with actual major-career combinations in the 2017-2019 American Community Survey. Conlon and Patel then use the CIRP Freshman Survey, covering more than nine million first-year students between 1976 and 2015, to compare students’ expected careers with the occupations subsequently observed among graduates from the same cohorts. In that, they find:

...large, systematic, and persistent differences between the careers that freshmen expect to attain and the actual occupations they go on to have... We see that twice as many students expect to become artists, counselors, and lawyers (about 5% each) than actually do (2%–3% each). Four times as many students expect to become writers and doctors (2.7% and 11.1%) than do (0.7% and 2.8%).

Interestingly, the occupations that students most overestimate themselves as having are those that are rare and representative of particular majors (like writers or artists), while those that are most underestimated are those that are common alternative occupations that many majors may later hold (like teaching or business). Conlon and Patel also show using implicit association tests that people:

...strongly and systematically associate majors with their representative careers: implicit associations are 0.30–0.36 standard deviations higher for representative major–career pairs than for nonrepresentative pairs...

This supports Conlon and Patel’s interpretation that stereotyping contributes to students’ exaggerated beliefs about the connection between majors and the corresponding representative careers. Conlon and Patel then show using a theoretical model that stereotyping can increase misallocation of labour. What that means is that, given the occupation in which a graduate eventually works, that person might have been better off studying a different field. They also present suggestive evidence that links greater stereotyping with job dissatisfaction and regrets about the chosen field of study.

The welfare loss, job dissatisfaction, and regret, then motivated a survey experiment where Conlon and Patel attempt to correct for the stereotyping. The survey experiment proceeded as follows, using students from Ohio State University:

The survey began by asking students the percent chance that they would graduate with the two majors they selected as being most likely to pursue (their “top-ranked” and “second-ranked” majors). It then asked their self and population beliefs about the likelihood of each career group conditional on these two majors. Students were randomly sorted into a control group and a treatment group. Those in the control arm answered questions about their classes so far that semester and how they had (or had not) contributed to their major and career plans...

In the treatment arm, information modules provided students with the actual distribution of careers conditional on each of their top two majors according to data from the [American Community Survey]. For each major, it told them several headline numbers about the frequency of the careers they had listed as their most likely jobs if they graduated with that major...

Conlon and Patel then looked at whether assigning a student to the treatment group, where they received accurate information about the chance their top-ranked majors would lead to particular careers, affected students' beliefs about their own chances of working in each major's 'representative career'. They found that the information partially (but not fully) corrects students' misbeliefs about the chance of attaining the representative career, and that:

...reducing students’ self beliefs about their chance of having their top-ranked major’s representative job by 10 percentage points decreases intentions toward that major by 0.11 standard deviations (about 3.5 percentage points, p < .05).

So, when accurate information lowers students' beliefs about their chances of obtaining the representative career associated with their top-ranked major, their intentions towards that major, and their enrolment in that major, decline. Interestingly though, correcting beliefs about a student’s second-ranked major can make that alternative more attractive. The response therefore depends on both the information that students receive and how much they value the different careers.

That would seem to be good news for some university majors, where the 'representative occupations' are relatively common (e.g. accounting or marketing) and bad news for other majors where the 'representative occupations' are less common (e.g. journalism or film studies).

Where does economics fit in as a major? Sadly, Conlon and Patel don't answer that question, as they combine economics with accounting, finance, marketing, and other business fields. In that broad group, 73 percent of prospective majors expected to enter a business career, compared with 47 percent of graduates who actually did so. So economics may not escape stereotyping. It may simply be stereotyped as a route into 'business', rather than more narrowly as a route to a job called 'economist'. However, we don't know for sure from these results.

Would better information help prospective economics students to make better choices? On the one hand, it might deter students who see economics as a guaranteed path to one particular career, but it might attract others by showing that economics is not tied to a single occupation. Perhaps the problem for economics is not a lack of possible career destinations, but that its many potential destinations are less vivid than the single job title of 'economist'.

[HT: Marginal Revolution, back in 2022 when it was Conlon's job market paper]