Positive network externalities and the value of social media

How much is social media worth? In an interesting article in The Conversation earlier this week, Peter Martin (Australian National University) discussed the value of social media networks:

Social media is a problem for economists. They don’t know how to value it...

As the Australian Senate prepares to hold an inquiry into the impact of social media, economists meeting in Adelaide at the annual conference of the Economic Society of Australia have been presented with new findings about the value of social media that point in a shocking direction. They suggest it is negative.

That’s right: the findings suggest social media is worth less to us than the zero we pay for it. That suggests we would be better off without it.

Wait, what? There's a good that we willingly use that has negative value? People may not be purely rational decision-makers, but that will take some explaining. Here's what Martin wrote about that study:

Leonardo Bursztyn of the University of Chicago presented the findings in the keynote address to the conference...

They surveyed more than 1,000 US university students, asking a series of questions about TikTok, Instagram and Google Maps (more about maps later).

The first set of questions was designed to ascertain how much they would need to be paid (or would be prepared to pay) to be off TikTok and Instagram for a month...

The answers suggest users value these platforms a lot, on average by US$59 per month for TikTok and $47 for Instagram. An overwhelming 93% of TikTok users and 86% of Instagram users would be prepared to pay something to stay on them...

Then Bursztyn and colleagues asked a second set of questions:

If two-thirds of the students on your campus sign up to deactivate, how much would you need to be paid (or be prepared to pay) to sign up too?...

Most of the TikTok users (64%) and almost half of the Instagram users (48%) were prepared to pay to be off them, so long as others were off them, resulting in average valuations across all users of minus US$28 for TikTok and minus $10 for Instagram.

Martin notes that:

The finding is a measure of the extent to which many, many users hate TikTok and Instagram, even though they feel compelled to use them.

I'm not so sure. Maybe users dislike TikTok and Instagram that much, but there is an alternative explanation, which relates to something I cover in my ECONS101 and ECONS102 classes: positive network externalities.

An externality is the uncompensated impact of the actions of one party on someone else (a bystander). A positive externality is an externality that makes the bystander better off. For example, if I plant a nice flower garden in the front of my house, it makes me feel happy, but it also makes people walking by happier as well. The flower garden creates a positive externality for the people walking by (the bystanders). The flower garden creates value for those walkers.

A network externality means that the value of belonging to a network depends (in part) on the number of other people using the network. A positive network externality is one where the larger the size of the network, the more value each network user receives from belonging to the network. Consider Facebook as an example. If you were the only user of Facebook worldwide, you've got a place to store photos, or you can post notes to yourself. So, it provides you some value, I guess. But the value of Facebook really comes about because you can interact with your friends there. The more of your friends on Facebook, the more value it creates for you. We could tell a similar story about Instagram, or TikTok.

Now, let's consider Bursztyn's results in the context of positive network externalities. When users of TikTok or Instagram were surveyed and asked how much they would have to be paid to stop using those services for a month, they had to be paid a lot. Clearly, those services do provide value to the consumers.

Next, when the users were asked how much they would have to be paid to give up TikTok or Instagram if two-thirds of the students on their campus sign up to deactivate, the value evaporated, and became negative for many users (let's call that the 'core value' that the social network provides). Why? Because if fewer of their friends are using Instagram or TikTok, then the value from the positive network externality is much lower. They are not willing to pay much if the service doesn't provide any core value (or even negative core value), because few or none of their friends are using it (and so any positive network externality isn't enough to make it worthwhile for them to use the social network).

In The Conversation article, Martin talks about "fear of missing out" as driving the results (and so do Bursztyn et al. in the research paper that Bursztyn's keynote was based on), and that may be true to some extent, but I think that frames the total value that social networks provide in a unnecessarily negative way. We have to remember that the whole purpose of social networks is networking - interacting with others. Without the interactive and community elements, a social network might as well just be a static website. Moreover, some users do receive core value from the social network, even without the positive network externalities. 

However, it is interesting that the service (excluding any positive network externalities) of Instagram and TikTok has negative core value for many (but not all) consumers. Those consumers demonstrating a negative core value wouldn't be using the social network if their friends weren't using it. This demonstrates that while particular social networks have a stranglehold on users' attention right now, that dominance is somewhat fragile. If a new service comes along that provides a higher core value, and attracts away a sufficiently large number of users, then that can cascade into a new dominant service. That is exactly what happened when Facebook took over the social network space from MySpace, Friendster, and Bebo. Or what TikTok may be doing to YouTube right now. And this creative destruction of the dominant firms is likely to be further promoted by the 'enshittification' of these products.

We could use results like Bursztyn's to argue that social networks are a bad thing, because they provide negative core value for many consumers. Or, we could recognise that overall they do provide value, because if they didn't, then consumers wouldn't use them. Just because that value comes from the fact that many other users are also using the same network, that doesn't mean that consumers don't want these products or value them. It's just that the value mostly comes from the network, not from the product. And if someone can make a better product and create a new valuable network, then consumers will move onto this next new shiny network instead.

Read more:

• How much money would you accept to give up Facebook for a month?
• What is Facebook worth to you?

Frank DiTraglia on how to read econometrics papers

Reading economics research papers is hard work when you are doing it for the first time as a graduate student. That's why I try to get as many undergraduate students as possible engaged in some 'inspectional reading' (a term I learned from Marc Bellamare's book Doing Economics, which I reviewed here), through the Waikato Economics Discussion Group. The hardest papers to read are papers in econometrics. Hard, but unfortunately necessary for students and researchers who want to apply the latest methods to their analyses, or who want to understand fully the canonical methods in econometrics.

So, it was interesting to read Frank DiTraglia's advice recently on how to read econometrics papers. Econometrics papers don't really lend themselves to 'inspectional reading', because the purpose of reading them is to understand the methods, and the actual results and simulations used to illustrate those methods are often incidental to this purpose.

DiTraglia offers a number of tips for readers (targeted at graduate students, but of use to other readers as well), including:

1. Reading more recent articles that apply the method, or reading review articles, rather than reading the original paper that introduces a particular method;
2. Don't assume that you have to understand the whole thing when you read a paper, but focus on understanding the key ideas;
3. Don't assume that you're stupid if you don't understand the paper, because key details that the authors assume you know about may be left out of the paper (which means reading other articles is doubly important);
4. Try explaining the key ideas to someone else, because as anyone who has done any teaching can tell you, you only really recognise how little you really understand, when you go to try and teach it to someone else; and
5. Head straight to the simulation or empirical example, rather than getting bogged down in the equations.

DiTraglia offers other points of advice as well, but I think those are the key points for most people who aren't going to get deep into the weeds of how a particular method works. Personally, I can vouch for #1 and #5 as really helping me to understand some econometrics papers, but especially #1. In my experience, the first person to develop a particular method is often rubbish at explaining it. It is only the subsequent authors, applying the method themselves and writing papers (who are really doing #4 from DiTraglia's list when they write up their research), who are really helpful in understanding the method.

[HT: Both David McKenzie on the Development Impact blog, and Marginal Revolution]

Paul Blacklow on types of competition

In my ECONS101 lecture this morning, we covered market structures. In a weird coincidence, this new article on The Conversation by Paul Blacklow (University of Tasmania) was published today, also on market structures. As Blacklow notes, there are four main market structures:

In the most ideal, a perfectly competitive market, firms must use resources efficiently to produce what we consumers want at the lowest possible cost...

At the opposite extreme, in monopoly markets, there is only one seller of a good or service. Typically, there is some barrier preventing new firms from entering the market and driving prices down...

More common than monopoly is what’s called monopolistic competition, which is the market structure for many of our tech, entertainment and dining goods and services...

In Australia, many key goods and services are traded in oligopoly markets.

Oligopolies arise when a few large firms dominate a particular industry, such as supermarkets, domestic airlines, banking, mobile telecommunications, and petrol retailing.

I like Blacklow's description of the market structures, which is not dissimilar to how I describe them in class. If you are interested in learning more, I encourage you to read the article. However, in my view there are a couple of additional points to note, in relation to monopolistic competition.

First, monopolistic competition is actually more common than Blacklow suggests. He uses the examples of tech, entertainment, and dining, but the range of markets in which there is monopolistic competition is much broader than that. Monopolistic competition involves firms selling products that are differentiated from the similar products being offered by other sellers. When it comes to most of the goods that we buy, sellers are trying to differentiate themselves from their competitors who are selling goods that are otherwise very similar. As I see it, most markets are either an oligopoly (like supermarkets) or monopolistic competition (like service stations). [*]

Second, Blacklow focuses on firms differentiating their product by advertising, or research and development. Most differentiation is actually enacted through branding (which is not quite the same as advertising). [**] By branding their products, a firm can make their offering seem different to those of their competitors. For example, service stations are selling the same product (fuel), but differentiate themselves through their brand. Firms can also differentiate themselves spatially, by the location of their stores. Service stations do this as well. The make their offering different from their competitors because the location of their service stations are different. And, firms can also differentiate themselves through the range of products that they sell. Again, service stations provide a good example. Some have a minimal, convenience store range of products in-store. Others have a more upmarket cafe offering. So, there are many ways that firms can differentiate themselves from their competition, and once you realise this, you start to notice just how common monopolistic competition is.

*****

[*] There isn't really a stark distinction between an oligopolistic market and a monopolistically competitive market. Some more realistically, most markets are actually in some uncomfortable space in-between these two market structures. They may even change over time, sometimes being more oligopolistic, and sometimes more monopolistically competitive.

[**] Yes, a firm would advertise its brand as a way of differentiating its product. However, that is not the only purpose of advertising - it also changes consumer preferences, as I noted in this post from several years ago.

Unemployment and trans-Tasman migration

The New Zealand Herald reported earlier this month:

Record numbers of people leaving New Zealand to work in Australia could have a negative affect on the workforce over the medium-term.

A report by economic think tank Infometrics shows Australia’s rate of unemployment was lower than New Zealand’s in the first quarter of this year, which was a break from the average rate between 2014 and 2018 when Australia’s rate was 0.7 percentage points higher than New Zealand’s.

“There is a definite correlation between transtasman migration and the relative labour market performances in New Zealand and Australia,” Infometrics director Gareth Kiernan said in the report.

Correlation doesn't necessarily mean causation. The New Zealand Herald article's title is therefore misleading: "‘Drain’ leaves NZ’s unemployment higher than Australia". Now, there are two problems with the New Zealand Herald article here, especially in terms of the title. First, there could be reverse causation - higher unemployment in New Zealand, and lower unemployment in Australia, causing more migration, not migration causing changes in unemployment. To see why, consider the incentives for workers in New Zealand. If unemployment in Australia is lower than New Zealand, then if wages were similar, Australia would more a more attractive option. Workers would start moving to Australia. Wages are not similar though - they are higher in Australia. That increases the incentives to move from New Zealand to Australia even further. The takeaway is, though, that unemployment differences may be causing migration, not the other way around.

The second issue is that, based on a simple supply and demand model of the labour market, migration could affect unemployment in both countries, but the effect would be in the opposite direction to what the New Zealand Herald suggests. To see why, consider the diagrams below, which show the labour markets of Australia on the left, and New Zealand on the right. In both labour markets, the market wage (W1 in Australia, and WB in New Zealand) is above the equilibrium wage (W0 in Australia, and WA in New Zealand). This means that there is excess supply of labour in both countries. There are more people wanting to work than there are jobs available. That is, there is unemployment in both countries. This excess supply of labour is the difference between QS1 and QD1 in Australia, and the difference between QSB and QDB in New Zealand.

Now consider what happens as workers more from the New Zealand labour market to the Australian labour market, as shown in the diagrams below. Supply of labour decreases in New Zealand from SLA to SLC, and at the market wage, the quantity of labour supplied decreases to QSC. This decreases the excess supply of labour in New Zealand (to the difference between QSC and QDB), so unemployment decreases. In the Australian labour market, the supply of labour increases from SL0 to SL2, and at the market wage, the quantity of labour supplied increases to QS2. This increases the excess supply of labour in Australia (to the difference between QS2 and QD1), so unemployment in Australia increases. So, the migration of workers from New Zealand to Australia should have the effect of decreasing unemployment in New Zealand, and increasing unemployment in Australia, not the reverse.

Now, there are many alternative models of the labour market, aside from the model based on supply and demand for labour. However, I don't think those alternatives would suggest decreases in labour supply would increase unemployment. For example, in a search model of the labour market, fewer available workers in New Zealand might mean that job vacancies remain unfilled for longer, since it would take employers longer to find a suitable worker, but unemployment would be unaffected (on the other hand, wages would increase, because with fewer workers available, each worker has slightly higher relative bargaining power).

So, there may be a correlation between unemployment differences between Australia and New Zealand, and trans-Tasman migration. But that doesn't mean that the migration will make unemployment differences worse.

Regional resilience to the Global Financial Crisis and Covid-19 shocks in New Zealand

This week the Waikato Economics Discussion Group discussed this article by William Cochrane, Jacques Poot, and Matthew Roskruge, published in the Australasian Journal of Regional Studies (open access). This paper won the John Dickinson Memorial Award for the best paper published in AJRS last year. In the paper, Cochrane et al. look at the take-up of social security benefits in New Zealand territorial authorities (the lowest administrative level of government in New Zealand) as a result of two shocks: (1) the Global Financial Crisis (GFC) in 2008-09, and the Covid-19 pandemic in 2019-20. Interestingly, comparing those two shocks they note that:

...the initial impact of the GFC on social security benefit uptake was of a similar magnitude to that of the COVID-19 pandemic: a mean increase across TAs of 1.86 per cent versus 2.23 per cent respectively.

A small gripe is that those are actually percentage point increases, not percent increases. In other words, social security benefit uptake was 1.86 percentage points higher after then GFC than the year before, and was 2.23 percentage points higher during the Covid-19 pandemic than before. Importantly, not only is the increase in social security uptake similar for the two shocks, but the spatial distribution of the uptake of social security benefits is similar for the two shocks, as shown in Figure 2 in the paper:

The areas that are shown in darker blue had larger increases in social security uptake as a result of the shock (the GFC is the map on the left, and Covid-19 is the map on the right). It is clear from the figure that the shocks were more keenly felt in the North Island. In particular, Northland is heavily affected by both shocks, as well as the eastern Bay of Plenty and East Cape.

Cochrane et al. then turn their attention to looking at the factors associated with the increase in social security uptake, asking the question, what factors are associated with greater resilience (that is, what factors are associated with a lower increase in social security uptake). To do this, they rely on Census variables taken from the 2006 Census (three years before the GFC), and the 2018 Census (two years before the Covid-19 pandemic).

Since Cochrane et al. only have 66 observations of change for each period, and over 140 Census variables, this poses a bit of a problem. Cochrane et al. solve this issue in a few ways. First, they categorise their variables into 15 categories, and use just one variable in each category in separate cross-sectional regression models for each shock, and in spatial panel regression models that combine the data across both shock periods. Then, in a separate analysis they use a machine learning algorithm to select the most important variables for inclusion in the model.

The variables that are statistically significantly associated with social security benefit uptake vary somewhat between the models, but there are two variables that are consistently significant. First, territorial authorities that had a lower unemployment rate two years prior to the shock had lower benefit uptake. Second, territorial authorities that had a higher proportion of public sector employment had a lower benefit uptake. From the post-estimation regression model after machine learning, a one percentage point higher unemployment rate in the previous Census was associated with a 0.268 percentage point higher social security benefit uptake. A one percentage point higher public sector employment rate was associated with a 0.076 percentage point lower social security benefit uptake.

The implications of this (if we can interpret these effects as causal), is that if central (or local) government wants regions to be resilient to shocks, then finding ways of reducing unemployment (difficult) or increasing public sector employment (perhaps less difficult) are important things to consider. [*] However, as Cochrane et al. note in their conclusion, the current New Zealand government may actually be doing harm to resilience, because:

...if austerity measures were to be introduced in future years that lead to less public sector employment across all regions, either to reduce public debt or to fund tax cuts, our results do point to a likely decline in regional resilience.

*****

[*] An important consideration here is the definition of public sector employment. This isn't clarified in the paper (and I guess I could ask the authors, given that I know them all quite well, so I will). Table 1 in the paper tells us that public sector employment is, on average, about 14 percent. That is clearly more than just those included in the 'Public Administration and Safety' industry in the ANZSIC classification, which was about 5.4 percent of employment in the 2018 Census. But it is similar to the total of that category plus 'Health Care and Social Assistance', which was 14.9 percent of employment in the 2018 Census. However, if you were to include the health sector in public sector employment, why would you not also include 'Education and Training' as well (bringing the proportion to 23.0 percent in the 2018 Census)?

This week in research #33

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

• Stefkovics and Sik (open access) find that interviewer happiness affects research participant happiness in face-to-face surveys, likely leading to bias in measures of happiness from those surveys
• Eisenbruch et al. find that Americans can accurately perceive foraging ability from faces of members of two traditional subsistence societies (the Hadza and the Tsimane)

And from my own research:

• With a large team led by Peter Newman (University of Toronto), our new article (open access) in the journal PLoS ONE uses a discrete choice experiment to investigate the willingness-to-pay for three HIV prevention technologies (oral pre-exposure prophylaxis (PrEP), rectal microbicides, and HIV vaccines) among men who have sex with men in India, finding that in each case, efficacy is the most important characteristic of the prevention technology (this is part of an ongoing collaboration between Peter and I, along with others, going back a decade or more, and builds on this previous published paper (with ungated version here) using the same dataset, which looked at PrEP alone)

Higher council rates lead to higher property rents

The New Zealand Herald reported earlier this month:

Big increases in council rates may be contributing to a rent crunch in some parts of the country.

Data from CoreLogic shows the regions with the largest increase in rent since the end of the Covid freeze in 2020.

Ōakura, New Plymouth, is top of the list with an increase of 68% compared to September 2020. It is followed by Marfell, in the same area, at 67%. Westmorland, Christchurch, is third at 66% and Lower Shotover, Queenstown, at 64%.

Kiwibank economist Sabrina Delgado said there was a “very strong theme” of council rates rises being passed through to higher rents.

“Ōakura was one of the suburbs which saw an over 20% increase in council rates last year while Marfell saw a 17% increase in rates.”

In my ECONS102 class this week, in addition to covering economic welfare (as noted in yesterday's post), we covered the (rental) market for land. A couple of things make this market different from the regular markets we draw diagrams for. First, in theory, the supply of land is fixed. As Mark Twain once joked, when it comes to land, "they aren't making it anymore". However, in practice, we do make more land. Land is reclaimed from the sea, or swamps are drained, or conservation land is released. So, the supply of land isn't fixed, but it doesn't respond much to a change in rent. In other words, land supply is very inelastic. Second, we need to consider landowners who are owner-occupiers. In effect, those landowners are renting land to themselves. They aren't actually paying rent to themselves, but they still face a cost of using their land - there is an opportunity cost, because they could have rented the land to someone else, and they are giving up the rent that they could have received. Owner-occupiers would still rent land to themselves even if the rent was zero, so the supply of land is positive, even if the rent is zero.

Now, let's turn to the situation in the article, and show why increases in council rates lead to higher rents. This is shown in the diagram below. Before councils raised rates, the market is operating at equilibrium, where the supply of land SN0 meets the demand for land DN0. The equilibrium land rent is R0, and the equilibrium quantity of land rented is QN0. The increase in council rates raises the costs of landlords, shifting the supply curve up and to the left, to SN1. The new equilibrium rent is R1, and the equilibrium quantity of land rented is QN1. [*] So, the increase in council rates is passed onto tenants in the form of higher rents.

*****

[*] Now, you might wonder about the decrease in the quantity of land rented. Where does it go? Notice though that the shift upwards in the supply curve, which represents the increase in costs to landlords, is quite large (shown by the red arrow), while the decrease in quantity of land rented is small by comparison. So, there isn't much change in the quantity of land. Where does it go? Some landlords may leave land fallow, and no longer use it or rent it out. Some may be converted to conservation uses. Either way, that (small amount of) land is no longer available to rent (even to an owner-occupier themselves).

Fast food operators under threat from weightloss drugs

This week my ECONS102 class has been covering (among other things) economic welfare, and last week we covered the model of demand and supply. Interestingly, this NBR article from yesterday (paywalled) gives us an opportunity to apply both of these things:

But it hasn’t stopped Shoeshine’s ears pricking up hopefully at the tidal wave of stories about the ‘miracle’ drug Ozempic, made by Denmark’s Novo Nordisk, the drug initially developed to treat diabetes and now used ‘off label’ across the world to lose weight. The success of Ozempic, and sister drug Wegovy, a USFDA-approved treatment aimed exclusively at weight loss, has been astronomical...

It’s a wave of obesity solutions that’s not yet hit New Zealand, says Dr Luke Bradford, medical director for the Royal New Zealand College of General Practitioners. But, he adds, “it will” – particularly after the patent on pack-leader Ozempic comes off around 2031.

While Ozempic is not available in New Zealand at present, and GPs are prescribing slightly older versions for Type 2 diabetes (Saxenda and Victoza), Bradford said it was a matter of time as supply chains and production lines get up to speed; and as production starts to include oral versions that are easier again to ship.

“These drugs are incredibly effective at what they do,” he says...

For the majority of people, however, the main side effect of taking the likes of Ozempic and Wegovy is that their appetite is substantially reduced, as is their desire for and ability to handle alcohol. Reddit boards that canvas the effect of taking Ozempic are full of people suffering extreme illness after gorging on fast or fatty food (which reacts badly with the drug); enduring ‘hangovers from hell’ (alcohol irritates the stomach lining and exacerbates the drug’s digestive effects); and toilet adventures that don’t bear repeating...

Most companies involved in the fast-food area, as well as the likes of global players including Nestlé and PepsiCo, are looking at how they might need to pivot in this new era of drug-induced weight loss. McDonald’s, which lost 16% in value last year (the result of a number of factors including a cost-of-living crisis, higher input costs affecting profits, a global boycott, changing diets) is among companies investigating healthier, smaller options for weight shedding clientele.

One might think New Zealand’s Restaurant Brands, owners of KFC, Taco Bell, Pizza Hut, and Carl’s Jr across New Zealand, Australia, California, and Hawaii might also be looking at this trend and wondering what it might do to its bottom line.

I don't think that we need to spend too much time wondering, and a consideration of the demand and supply model, and economic welfare, can give us an answer (albeit not a perfect answer). Consider the market for fast food, as shown in the diagram below. The market starts in equilibrium, where the demand curve D0 intersects the supply curve S0, with Q0 fast food traded, at an equilibrium price of P0. Decreased calorie demand from consumers, alongside some pretty awful side effects from consuming fatty foods when using Ozempic or Wegovy, decreases the demand for fast food from D0 to D1. This reduces the equilibrium price of fast food to P1, and decreases the quantity of fast food traded to Q1.

Now, think about what this means for the producers of fast food. Producer surplus is the benefit that sellers get from operating in the market. It is the difference between the price that the sellers receive, and their (marginal) costs. On the diagram above, producer surplus is initially the area P0AC. However, after demand decreases, the producer surplus decreases to the area P1BC. Clearly, the sellers of fast food are made worse off by this change.

It's little wonder that fast food brands' share prices have been tumbling of late. For example, here's Restaurant Brands' share price over the last five years (source here):

For reference, the drug Wegovy was released in 2021, while Ozempic was released in 2017, but was approved for use in weight loss in 2021. Notice that is about the time that the Restaurant Brands share price starts to fall. Share prices reflect markets' expectations about future profits and cashflows (at least, in theory). The falling share prices reveal that the market believes that future profits and cash flows will be lower, possibly because effective weight-loss drugs (especially those that have awful side effects for people eating fatty foods) are going to harm those firms' profits.

Peter Gray on the social media-mental health debate

The debate about whether social media has a causal negative impact on mental health, recently inflamed by Jonathan Haidt's book The Anxious Generation (see here and here), continues to rage. In the latest contribution, Peter Gray posted:

When I read, at Jon’s request, a pre-publication draft of the book, I told him I could not support it, and I explained why. I had at that time already looked quite broadly and deeply at the research pertaining to questions about effects of screens, Internet, smartphones, and social media on teens’ mental health and found that, despite countless studies designed to reveal such harmful effects, there was very little evidence for such effects. If you survey the research literature selectively, with an eye toward finding studies that seem to show the effects you are looking for, and if you don’t analyze them critically, you can make what will seem to readers to be a compelling case.  But people who really know the research and have examined it fully and critically will see through it.

Gray summarises several previous posts he has written on the topic, which seem to go against Haidt's evidence that social media caused an increase in teen mental health problems. Gray then focuses on one key part of the evidence base of Haidt's book, which is the sole randomised controlled experiment that Haidt uses:

On pages 147-148 of The Anxious Generation, Jon claims that random assignment controlled experiments have shown that social media is a cause (not just a correlate) of teen suffering. He cites just one example of such an experiment, so I looked it up and read the article. The reference, if you want to look it up, is this: Melissa Hunt et al (2018), “No more FOMO: Limiting social media decreases loneliness and depression. Journal of Social and Clinical Psychology, 37, pp 751-768.

The research participants in this experiment were 143 undergraduate students randomly assigned to either limit Facebook, Instagram and Snapchat use to no more than 10 minutes per day, per platform, or to use social media as usual for a three-week period. Self-report questionnaires were used to assess various indices of subjective well-being, namely their sense of social support, fear of missing out, loneliness, anxiety, depression, self-esteem, autonomy, and self-acceptance before, during, and after the three-week period.  The researchers also assessed the participants’ actual social media usage throughout the study by requiring them to submit screen shots showing accumulated use of these media.

The most damaging flaws with this study, which should be obvious to any social scientist, is there are no controls for demand effects or placebo effects. I’ll describe these separately.

Social scientists have shown repeatedly that when research participants can guess the purpose of an experiment and guess the researchers’ hypothesis, they are generally motivated, consciously or unconsciously, to support that hypothesis. In other words, they are likely to believe, or at least claim, they are experiencing what they assume the researcher expects them to experience, to prove the hypothesis correct. This is called the demand effect...

Now the placebo effect. This refers to the simple fact that when people believe they are doing something that will make them feel better, that belief by itself makes them feel better.

Gray points out that the sole study that Haidt relies on to show causal effects of social media on mental health is highly likely to be subject to both the demand effect and the placebo effect, and therefore cannot be relied on as strong evidence of what it claims to be showing. The debate on this book and its thesis is looking increasingly like resolving against the book's claims, or at the very least against the strength of its claims.

[HT: Marginal Revolution]

Read more:

• Quitting Facebook might make you happier
• The more you use Facebook, the worse you feel
• Does the Internet make people happier?
• What happens when you disconnect from Facebook?
• The latest NZ research on social media and mental health is nothing special
• Online social networks, social capital, and wellbeing
• Jonathan Haidt and Candice Odgers debate the relationship between social media and mental health
• Mike Masnick on the social media-mental health debate

Book review: The Economics Book (Big Ideas Simply Explained)

There are many books on the market that try to make economics accessible to a general audience. Some books try to do so using economics research applied to real-world situations (most famously Freakonomics). Others try to unpack economic theory and models and explain them, often but not always through the lens of the history of economic thought (such as The Economics Book by Steven Medema, which I reviewed here; or New Ideas from Dead Economists, which I reviewed here).

The unimaginatively titled The Economics Book, part of the Big Ideas Simply Explained series and written by a large group of contributors (including Niall Kishtainy, George Abbot, John Farndon, Frank Kennedy, James Meadway, Christopher Wallace, and Marcus Weeks), takes the second approach. And like Medema's identically titled book, this book devotes a page or two to each idea, moving more-or-less sequentially through time, with some grouping of similar ideas.

However, while I really liked the approach in Medema's book, I don't think it worked nearly as well in this book. Some of the ideas have too much overlap with others, and so there was occasional unnecessary repetition. There were some ideas early on that seemed to be presented in a surprisingly uncritical way, like the labour theory of value. They were challenged in later parts of the book, but a reader who just dipped into a section or two could easily come away thinking that some old ideas retain currency in contemporary economics, when that is not the case at all.

The book has some excellent aspects though. It contains a number of sections that have a more modern economic debate feel about them, such as inequality and growth, international debt relief, and global savings imbalances. It also contained a two-page section on gender and economics, where New Zealand's own Marilyn Waring was the star. However, it was somewhat surprising that Claudia Goldin (who won the Nobel Prize last year) wasn't mentioned in that section. You could argue that the book was published in 2012, and the authors couldn't foresee Goldin's later award, but they did have sections devoted to other recent Nobel Prize winners, including Diamond, Dybvig, Bernanke, Nordhaus, and Thaler, among others.

There were also aspects of the writing in the book that I disliked, including slight inaccuracies in language. For example, economists refer to 'comparative advantage', not 'competitive advantage'. And, when the price of a good increases, it is 'quantity demanded' that decreases, not 'demand'. And Stephen Dubner is a journalist, not an economist, despite being one of the co-authors of Freakonomics.

Despite those gripes, this was an interesting book to read. I wouldn't rate it as highly as Medema's book, and there are several others that I would recommend ahead of it. However, for someone looking for additional reading that tries to make economics accessible, this would be a good addition to a reading list.

This week in research #32

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

• Hess (with ungated earlier version here) finds that, after the US Supreme Court's decision in Dobbs (which allowed states to restrict abortion access), medical schools in states with abortion bans saw a 0.65% increase in the 2022 share of women’s applications and a 1.17% increase in the 2023 share, plausibly driven by applicants interested in an OB/GYN specialty
• Kahane finds that NHL players are willing to sacrifice earnings to play on a higher quality team, and this tradeoff increases with player age
• Burdett et al. (open access) investigate workers' self-reported productivity during COVID-19, and find that workers report being at least as productive as before the pandemic, but with substantial heterogeneity (unsurprisingly, there are negative impacts for female parents)

And from my own research:

• With Jacques Poot, our new article (open access) in the journal Population Research and Policy Review investigates the use of spatial interaction models (a generalisation of gravity models) for forecasting internal migration flows in New Zealand, showing that these models may be useful for population projections (this work builds on a long stream of research by Jacques and I in this area)

Food price rises and consumer choices

This week my ECONS101 class has been covered constrained optimisation, with a particular focus on consumer choices. So, I was interested to see this article by Puneet Vatsa and Alan Renwick (both Lincoln University) in The Conversation yesterday, talking about food price rises:

The rising price of food has been making headlines for the past decade. But prices have not been rising consistently across all food groups – and this has major health implications for New Zealanders...

Although food price increases have been noticeable over the long term, the change in relative prices — the cost of one food category compared to another — often goes unnoticed. Nevertheless, these relative price changes are crucial as they influence consumer choices, often subconsciously.

Our new research examines Stats NZ data between 2014 and 2023 on the price of 85 food items collected from 560 retail outlets – supermarkets, greengrocers, fish shops, butchers, convenience stores, restaurants, and outlets selling breakfast, lunch, and takeaway foods – in 12 urban areas.

Between July 2014 and March 2023, prices of some sweetened, processed foods and drinks such as boxed chocolate, ice cream, soft drinks and sports energy drinks have risen by around 14%. At the same time, price of some fruits and vegetables have risen by around 45%.

When sweetened processed foods are cheaper relative to fruits and vegetables, people tend to buy more of the former. This can lead to poor dietary habits, increasing the prevalence of obesity and related health issues.

Changes in relative prices are something that we can examine theoretically using the consumer choice model (otherwise known as the constrained optimisation model for the consumer). I've used this model to describe consumer choices before (see here and here), but this application is slightly more difficult, as the price of both goods is changing over time. I'm not going to go into the basics of the model. It has two components - budget constraints (which I explain here) and indifference curves (which I explain in some detail here).

Consider a model of consumer choices, where the consumer can choose between two goods: sweetened processed foods (S) and fruits and vegetables (F). This model is shown in the diagram below. The consumer has income of M, the price of sweetened processed foods is PS0, and the price of fruits and vegetables is PF0. The consumer's budget constraint is shown by the straight, downward sloping line. The slope of the budget constraint is equal to [-PF0/PS0] (which is the relative price of the two goods). The consumer's best affordable choice (the consumer's optimum) is the bundle of goods E0, which is on the highest indifference curve that they can reach, I0. That bundle of goods includes S0 units of sweetened processed foods, and F0 units of fruits and vegetables.

Now consider changes in prices. The price of sweetened processed foods increases (to PS1), and the price of fruits and vegetables increases (to PF1), but the increase in the price of fruits and vegetables increases by more than the increase in the price of sweetened processed foods. The consumer's budget will not be able to buy as much as before (their income is still equal to M [*]), so the budget constraint will move inwards. The budget constraint will also become steeper, because the relative price has changed. The price of both goods has increased, but the price of fruits and vegetables has increased by more than the price of sweetened processed foods. So, the new slope of the budget constraint, which is equal to [-PF1/PS1], must be a larger number (because PF is increasing faster than PS), meaning that the budget constraint is steeper.

This change is shown in the diagram below. The new budget constraint is the red line - it is steeper and moved inwards from the original black budget constraint. The consumer can no longer buy the bundle of goods E0, because it is outside the budget constraint (the consumer cannot afford to buy E0 anymore). The consumer's new best affordable choice is the bundle of goods E1, which is on the highest indifference curve that they can reach now, I1. That bundle of goods includes S0 units of sweetened processed foods, and F0 units of fruits and vegetables.

Notice that the consumer buys less of both goods, but the impact on the quantity of fruits and vegetables is greater than the impact on the quantity of sweetened processed foods, which is what Vatsa and Renwick found in their research. [**]

*****

[*] To make life a bit easier, we're assuming that the consumer's income has not changed. Effectively, this reflects an assumption that the price of both goods is increasing faster than incomes have increased, which is probably not too far from recent experience in New Zealand, where food price inflation has been higher than wage inflation.

[**] It is also possible to draw this diagram in such a way that the impact on the quantity of fruits and vegetables is less than the impact on the quantity of sweetened processed foods. The only difference in the diagram would be the placement of the new highest indifference curve I1 and the new best affordable choice E1, which would be further to the right on the diagram. I'll leave that as an exercise for any interested reader to do for themselves.

There's something about Wellesley

The gender gap in economics has drawn a lot of attention in recent years (see the list of links at the end of this post), and there are many efforts underway to address the gender gap. Given the pervasiveness of the gender gap, and the difficulties the profession is facing in addressing it, it is interesting to note examples that provide some (maybe any) evidence of substantial success in reducing the gender gap.

So, I was interested to read this new article by Kristin Butcher, Patrick McEwan and Akila Weerapana (all Wellesley College), published in the journal Feminist Economics (ungated earlier version here). They look at all admitted applicants to Wellesley College, a private liberal arts college in Massachusetts, and essentially compare admitted applicants who followed through and enrolled at Wellesley with admitted applicants who did not (and enrolled at some other university or college). That comparison tries to answer the question of, what is the impact of enrolling at Wellesley College? The key outcome variable is whether the students enrol in an economics major, but Butcher et al. also look at enrolment in a number of other majors.

Butcher et al. have a sample of over 15,000 admitted applicants over the period from 1999 to 2013, which they gather graduation data from the National Student Clearinghouse for, and then link to high school data for each student. Their preferred analysis controls for a range of student-level characteristics (including admission board rating) and school-level characteristics. Overall, they find that:

...Wellesley College enrollees are 6.2 percentage points more likely to major in Economics, relative to a comparison-group mean of 7.7 percent. The estimate increases to 7.2 percentage points with a full set of applicant and high school controls.

That is an enormous impact, implying that enrolling at Wellesley College nearly doubles the probability that a student enrols in an economics major. Looking at other majors, Butcher et al. are able to show that students enrolling at Wellesley are not less likely to take STEM majors, so economics is not poaching students from other quantitative disciplines - at least not at Wellesley. There is evidence that enrolments in any other maths-intensive business major (business/managerial economics, finance, or management sciences) and engineering decrease. However, this should not be surprising because neither of these options are offered at Wellesley.

The enrolment effects also show through in other variables, such as:

Only 0.27 percent of non-enrollees complete a graduate degree in Economics, but Wellesley enrollment more than doubles the probability by 0.32 percentage points.

They also find that Wellesley enrollees are more likely to receive a National Science Foundation Graduate Fellowship. However, this is a rare event, and the results are not statistically significant. 

Now, given that all students at Wellesley are female students, the massive increase in economics majors (and subsequently in graduate degrees) is a potentially important finding that is worth exploring further. So, Butcher et al. try to look at what factors might explain this large enrolment effect. They use a decomposition analysis to look at the contribution of different factors to the overall effect. This reveals that 60 percent of the greater enrolment in economics majors is explained by observable 'gender-related and non-gender-related college variables'. Focusing only on the gender-related variables, Butcher et al. note that:

Gender-related college variables include an indicator of women’s colleges, the proportion of women students, and the proportion of women faculty in the Economics Department and college-wide... The variables capture the varied effects of exposure to women classmates, mentors, and instructors. Collectively, the gender-related college variables explain –0.032 of the reduction, which is statistically distinguishable from zero at conventional levels. This is 44 percent of the estimated gap of 7.2 percentage points between Wellesley enrollees and non-enrollees.

The other 16 percent of the effect that is explained by college variables is made up of:

...the public or private status of the college, its focus on undergraduate versus graduate education, and the presence of higher- or lower-achieving peers of any gender, as proxied by math SAT scores...

That still leaves 40 percent of the effect unexplained by observable differences between Wellesley College and other colleges and universities. That is a lot to just be left as the 'unexplained effect of Wellesley College on students enrolling in economics majors'. The disappointing thing is that this is a substantial impact, and we really want to know what is causing it. Butcher et al. offer some speculations, including a greater focus on teaching in tenure and promotion decisions, and an anti-grade-inflation policy that was implemented in 2004. I didn't find those speculations very persuasive (and to be fair, they probably weren't meant to be). This is a case where some additional qualitative work could be used to investigate students' decision-making on major choice at Wellesley, to try and tease out what drives them to choose economics in such greater numbers. That might help other universities to learn from their example.

[HT: Marginal Revolution, last year]

Read more:

• The gender gap in economics
• More on the gender gap in economics
• CORE on 'missing women in economics'
• Female representation in economics
• Justin Wolfers on why we should care about the gender gap in economics
• Why we should care about the gender gap in economics
• Could closing the gender gap in economics be as simple as providing students with information?
• A sobering report on the culture in the economics profession
• The gender gap in reviewing and editing for top economics journals
• The gender gap in U.S. economics education
• UWE and the gender gap in economics (results pending?)
• Three papers on the gender gap in economics seminars
• Disappointing results on simple information interventions to close the gender gap in economics
• Gender differences in fields of specialisation within economics
• Gendered citations of papers in the 'top five' economics journals
• The positive effect of mentoring for female economists
• What machine learning is telling us about the gender dynamics in economics seminars

Climate change is coming for your morning caffeine fix

The Financial Times reported last week (paywalled):

The price of coffee is set to rise from its current record high as climate change, shipping disruptions and new EU regulations drive up costs for roasters, Italian coffee giant Lavazza has warned...

However, Giuseppe Lavazza, chair of Lavazza Group, which owns Lavazza coffee, said the price of coffee on UK supermarket shelves, which is already up by about 15 per cent this year, could rise close to another 10 per cent by next year...

Coffee roasters such as Lavazza have been forced to put up prices and reduce profit margins as the cost of the raw material has surged, said Lavazza, who is the fourth generation to head the Turin-based coffee group...

“Climate change has affected the production in the most important robusta countries around the world, mainly Vietnam and Indonesia, reducing quite a lot the quantity available of these kinds of varieties.”

Weather forecasts suggest the next Vietnamese harvest will fail to replenish waning supplies of robusta coffee beans, which are used in espresso and for instant coffee.

To see what is going on in the coffee market, we can use the simple model of supply and demand (which I'm covering in my ECONS102 class this week). Consider the coffee market, shown in the diagram below. Last year, the market was operating at equilibrium, where the demand curve D0 intersects the supply curve S0. The equilibrium price of coffee was P0, and Q0 coffee was traded. The poor coffee production due to climate impacts decreases the supply of coffee from S0 to S1. As a result, the equilibrium price of coffee increases to P1, and less coffee (Q1) is traded.

Some consumers may be tempted to switch from coffee to tea (coffee and tea are substitutes). However, there is little respite from the higher prices to be found in tea. Even putting aside the fact that there might be negative climate impacts on tea production, the higher price of coffee, and the switching of consumers from coffee to tea, will likely drive up the price of tea as well.

To see why, consider the market for tea, shown in the diagram below. Last year, the tea market was operating at equilibrium, where the demand curve DA intersects the supply curve SA. The equilibrium price of tea was PA, and QA tea was traded. Consumers switching from coffee to tea increases the demand for tea from DA to DB. This leads to an increase in the price of tea to PB, and an increase in the quantity of tea traded, to QB.

Climate change has a lot to answer for, and now it's coming for your morning caffeine fix. 

How much is your job worth to you?

A rational decision-maker weighs up the cost and benefits of the alternatives available to them before they decide which alternative is the best option for them. When faced with a 'yes or no' decision, 'yes' is the best alternative when the benefits outweigh the costs (and 'no' is the best alternative when the costs outweigh the benefits). When choosing between mutually exclusive alternatives, the best alternative is the one that provides the greatest net benefit (the difference between benefits and costs).

The costs and benefits might be monetary, but not necessarily. And even if the costs and benefits are not directly monetary, they may still be measurable in dollars. For example, how much is your job worth to you? It seems like an odd question to ask. You didn't 'buy' your job, after all (I hope!). But, as we will come to a bit later, this question has some important policy implications.

How can we work out how much a job is worth to the worker? Since the worker has their job already, we can't use how much they are willing to pay to get a job. However, we can try to find out how much the worker would be willing to accept in order to quit their job. So, how much would you have to be paid to quit your job?

That is the question that Soumaya Keynes asks in this recent article in the Financial Times (paywalled):

A new working paper by researchers at the Centre for Economic Policy Research and Stanford University, deploys this approach, asking Europeans what they would do if they received sums ranging from €5,000 to €100,000.

Below around €25,000, people say they would plough on with work. But for sums between that threshold and €100,000, their likelihood of working falls by 3 percentage points on average. Women, as well as people who are older, who have less debt or who are close to retirement are more likely to drop out.

What does that imply about the value of a job? If paying someone €100,000 reduces their likelihood of working by three percentage points on average, then reducing their likelihood of working by 100 percentage points would cost €3.33 million (about NZ$5.85 million). [*]

Why does this matter? Keynes notes that:

The question of how one might respond to a financial windfall of this sort is a fun thought experiment. But for policymakers it carries more weight. They have to consider whether a stimulus cheque or a tax break could encourage people to quit their job, or make them deaf to pleas from desperate employers. They have to ask how much money it takes to turn someone idle.

It seems like it would take a substantial windfall to cause most people to quit their jobs, beyond the scope of what a stimulus cheque, or even a universal basic income, would provide. That doesn't mean that no one will quit after receiving even a modest windfall, but policymakers can probably rest easy about the labour market disincentive effects of windfalls.

*****

[*] Now, my ECONS102 students should recognise that this amount is probably an overestimate of the 'true value' of a job to a worker. Like all decision-makers, on average workers are loss averse - they value losses much more than otherwise-equivalent gains. One consequence of loss aversion is the endowment effect - decision-makers require more in compensation to give something up than what they would have been willing to pay to obtain it in the first place. This applies to jobs, as it does to other things. So, we might expect people to need to be paid more to give up a job, than what they would have been willing to pay to get the job in the first place. So, the estimate of €3.33 million is probably an overestimate of the 'value' of a job to a worker.

This week in research #31

Here's what caught my eye in research over the past week (a fairly quiet week on the whole):

• Candelon, Joëts, and Mignon (with ungated earlier version here) find that high topic connectivity augmented by robust social connectivity among authors or authoring teams enhance the diffusion of econometric ideas, based on a sample of 17,260 research papers in econometrics over the period 1980-2020
• Vella (open access) conducts a meta-analysis of studies linking the Big Five personality traits to earnings, and finds that openness to experience, conscientiousness, and extraversion exhibit positive correlations with earnings, whereas agreeableness and neuroticism are inversely correlated with earnings (no surprises in those results, I think)

Unintended consequences of visas for victims of serious crime

Following yesterday's post, and still on the topic of incentives, consider this example reported by CWB Chicago back in May:

Federal prosecutors on Friday announced charges against five people in connection with a Chicago-based scheme that staged armed robberies so the purported victims could apply for U.S. immigration visas reserved for legitimate crime victims...

Officials believe hundreds of people, including some who traveled from out of town, posed as customers in dozens of businesses across Chicago and elsewhere, all hoping to win favorable immigration status by becoming “victims” of pre-arranged “armed robberies.”...

Federal prosecutors said on Friday that each. purported “victim” paid “thousands of dollars” for the privilege of being robbed at gunpoint. Ringleaders then instructed the “victims” to be at a certain location at a specific time to be “robbed.”...

After the robberies, the “victims” went to their local police departments to secure documentation that they were the victims of a crime that qualified them to apply for a “U-visa.” That’s an immigration status reserved for “victims of certain crimes who have suffered mental or physical abuse and are helpful to law enforcement or government officials in an investigation or prosecution,” federal officials explained Friday. Some relatives of U-visa recipients also qualify for special status. In time, U-visa recipients may qualify for permanent residency.

Obviously, the policy to grant a visa to victims of serious crime was implemented in order to help those victims (as well as crime investigators, since the victims would remain available to provide witness statements and testify if a case goes to trial). The problem is that it changes the costs and benefits of being a victim of crime for those who are not already US citizens, and created unintended consequences.

Being a victim of a serious crime comes with obvious costs, but nothing much in the way of benefits. However, being able to claim a U-visa adds some benefits to being a victim of serious crime (for non-US citizens). That by itself is probably not enough to make someone want to be the victim of a serious crime. But what if they could be a 'victim of a serious crime', without being victimised by the crime? They would gain the benefit of the U-visa, without the psychological costs of being a victim.

Now, if the benefit of the U-visa is sufficiently valuable (and given the number of illegal border crossings into the US each year, it seems like living in the US is pretty attractive), a non-US citizen might even be willing to pay for the 'privilege' of being a 'victim of a serious crime'. And there you have it - an unintended consequence of the U-visa.

[HT: Marginal Revolution]

Incentives turn against term-time holidays for families

Both my ECONS101 and ECONS102 classes covered incentives this week (among other things). So, I was interested to see this article in the New Zealand Herald last week:

Families with school-aged children are choosing not to take their children out of school to travel, Flight Centre data has shown, as the Government cracks down on truancy.

Flight Centre New Zealand general manager Heidi Walker said it appeared the messages from the Ministry of Education were getting through to parents.

New data had shown travel bookings during the winter holidays were up 10% from last year, while travel during the school term had dropped 30%.

It comes after the Government announced in April it would start cracking down on truancy by introducing a “traffic light system” of punishments for students and their parents, including fines for parents and police referrals.

Incentives are rewards or punishments that influence the benefits and costs of the alternatives that a decision-maker can choose. In this case, parents can choose to go on holiday during the winter school holidays, or during term time. Going on holiday during term time means parents must take their children out of school.

Because of the Government's recent rule changes, parents now face the risk of punishment if they take their children out of school to go on holiday. The cost of term-time holidays has therefore gone up, because the 'full cost' of the holiday includes not just the price of the holiday, but also the risk of punishment for children missing school. When the cost of something increases, we tend to do less of it. The increase in the cost of term-time holidays leads families to take fewer term-time holidays (which have decreased by 30 percent, according to the article).

However, the policy change also affects holidays during the winter school holiday period, even though the full cost of those holidays has not changed. To see why, consider the relative price (or relative cost) of travel during school holidays compared with travel during term time. Since the cost of travel during term time has gone up, and the cost of travel during school holidays has remained the same, the relative price of travel during school holidays has decreased (travel during school holidays is now relatively cheaper than travel during term time, compared with before the policy change). When the relative price of something decreases, we tend to do more of it. The decrease in the relative price of travel during winter school holidays leads families to do more winter school holiday travel (which has increased by 10 percent, according to the article).

Incentives matter. They affect our decisions, by changing the costs and/or benefits of the alternatives available for us to choose.

This week in research #30

Last week I was at the 12th International Conference on Population Geographies at Queen's University in Belfast. Despite the name, this conference attracts a multi-disciplinary group of attendees across population geography, population economics, and sociology, among other fields. There were more sessions that I wanted to attend than I could possibly attend, but here are some of the highlights I found from the conference:

• My tong-term collaborator Jacques Poot presented on using spatial interaction models for forecasting inter-regional migration in Australia (related to work that Jacques and I have done for New Zealand, forthcoming in the journal Population Research and Policy Review)
• Tony Champion and Anne Green presented on an analysis of Graduate Outcomes Survey data from the UK, showing which regions gain and which lose migrants moving to and from university, which is also published in the journal Population, Space and Place (this would be interesting to look at for New Zealand as well)
• Erli Kang presented on the influence of the Hukou system on Chinese students returning from studying overseas (from which I learned that students who study abroad can claim Hukou status in Shanghai or other cities, if the university they studied in abroad has a high ranking, which neatly explains why international university rankings are so important for the Chinese student market)
• James O'Donnell presented counterfactual scenarios on how international migration affects regional populations and population ageing in Australia
• Nevena Trnavcevic presented on the challenges of ethnicity data in vital statistics in Serbia, where there are problems of inconsistency in recording ethnicity for many small ethnic groups (and ethnicity, multiple-ethnicity, and ethnic mobility were real themes across many sessions at the conference)
• Richard Wright gave an excellent keynote address on changes in racial-ethnic diversity in the US from 1990 to 2020 (with much of the data and results available online at https://www.mixedmetro.us/ (and again, this is something that could easily be replicated for New Zealand)

Aside from the conference, here's what caught my eye in research over the past week (a slow week, as I didn't have much time to keep up):

• Fenizia and Saggio find that city council dismissals in Italy increase employment, the number of firms, and industrial real estate prices, and the effects are concentrated in Mafia-dominated sectors and in municipalities where fewer incumbents are re-elected, suggesting that the dismissals generate large economic returns by weakening the Mafia and fostering trust in local institutions

Singapore Airlines' incentives for connecting passengers on late flights

I'm writing this post from a hotel in Singapore, during an unscheduled day-long stopover during my return to New Zealand from Belfast. It has been a bit of an experience, but interestingly it highlights a number of things related to incentives, which I'll be covering in both my ECONS101 and ECONS102 classes in their first week coming up.

Today (technically, it was actually yesterday, due to the time zone changes) started in an eventful way. We travelled from Belfast to London Heathrow yesterday and stayed overnight in the Hilton Garden Inn, which overlooks one of the runways and Terminal 2. When we woke up and looked out the window, we could see a massive crowd of people outside, with police and ambulance vehicles lined up at the entrance to Terminal 2. It turns out that Terminal 2 had been evacuated after someone had left a 'suspicious package' at check-in.

Fortunately for us, the evacuation was all over in about 90 minutes, and before we had to check in for our return flights, via Singapore to Auckland. However, the reopening of Terminal 2 was the start of absolute chaos (which you can see in the articles linked above). The check-in and security system at London Heathrow is usually running at full capacity at the best of times. Now factor in a 90-minute backlog of passengers trying to get through in time for their flights.

My wife and I arrived in plenty of time and, thanks to the fast track (and arriving early) we made it to our flight on time. However, others were not so lucky. Singapore Airlines kept the flight at the gate for an hour and twenty minutes, waiting for the last 40 passengers to clear security and get to the gate. Our plane then missed its take-off slot, and the Heathrow air traffic control tower then kept us parked at the gate for a further half hour waiting for a new slot to open up.

Finally, we were underway, but nearly two hours late. That doesn't sound too bad, but my wife and I (and a not insignificant number of other passengers) were supposed to be connecting with an Air New Zealand flight from Singapore to Auckland, which was due to leave one hour and twenty minutes after the originally scheduled landing time of our Heathrow to Singapore flight. I thought the pilots might try to make up some time during the flight (which I have experienced on other delayed flights in the past), but these pilots did nothing. We arrived in Singapore, and our flight to Auckland had already left. So, Singapore Airlines rebooked us on a later flight, and put us up in a hotel for the day. And we were not alone. There were dozens of affected connecting flights, and dozens of passengers that were rebooked and sent to hotels.

So that's my story. What does this have to do with incentives? As I will note in lectures for ECONS101 and ECONS102 this week, incentives for decision-makers to change their behaviour arise when the costs and/or benefits change. In this case, there are a number of changes in costs that affected Singapore Airlines' decisions.

Think about my story from Singapore Airlines' point of view. There were several choices they had to make. First, when they realised that a number of passengers were running very late for the Heathrow to Singapore flight, they had (at least) two alternatives to choose from. They could have the original flight leave on time (or close to it) and re-book the 40 late passengers on alternative flights. Or, they could wait for those late passengers. Clearly, re-booking passengers is expensive and time-consuming, and especially so for long-haul flights with connections that might also be missed. So, Singapore Airlines chose the least costly option, which was to delay the departure of the Heathrow to Singapore flight.

Second, once the Heathrow to Singapore flight was in the air, they could choose to amble along at regular speed, or try to make up time. Both of these options were going to be costly to Singapore Airlines. Making up time would use more fuel, and jet fuel is expensive (according to this website, it costs approximately £205,000 or NZ$430,000 to fill an Airbus A380 full of fuel). However, ambling along at regular speed entails costs in terms of re-booking passengers, as well as taxi and hotel costs for passengers re-booked on much later flights. I don't know the real costs involved here of course, but clearly Singapore Airlines believed that it was less costly to re-book passengers and face those costs, rather than paying for additional jet fuel. [*]

Finally, there are some long-term incentives for passengers here, and Singapore Airlines' decisions could be counterproductive to their long-term interests. Airlines want passengers to arrive at the airport early, check in and clear security. This gives them some certainty over the number of passengers flying, and baggage weight, which need to be known for calculating the fuel load required for the flight. However, Singapore Airlines' decisions today were to the benefit of passengers who arrived at the airport later, and imposed additional costs on passengers who arrived early (like my wife and I). The expected costs and benefits of future airline travel have changed. Perhaps we shouldn't bother to be so early to the airport in future, if airlines are simply going to hold the plane for us if we are running late? In the meantime, we'll enjoy Singapore Airlines' hospitality during this unexpected stopover, and I'll be thankful that I will still arrive in time for teaching on Monday.

*****

[*] Of course, there may be another possibility here, which is that the A380 didn't have enough fuel to operate fast enough to make an appreciable difference to the flight time. I'd be surprised by this though, as planes are required to carry additional fuel beyond what is necessary for the journey.