Is Uber a substitute or complement for public transport?

Is Uber a substitute or complement for public transport? You could make arguments either way. On one hand, passengers could use Uber instead of public transport. So, if Uber becomes more available or relatively less expensive, some passengers might switch to Uber for their commuting or other journey - in that case, Uber and public transport would be substitutes. On the other hand, passengers could use Uber to solve the 'last mile' problem. The could take public transport for most of their journey, and then use Uber to 'fill in' the first or last part of their journey, which public transport cannot provide - in that case, Uber and public transport would be complements.

So, which is it? Substitutes or complements? That is the question addressed in this 2018 article by Jonathan Hall (University of Toronto), Craig Palsson (Utah State University), and Joseph Price (Brigham Young University), published in the Journal of Urban Economics (ungated version here). They use data from US Metropolitan Statistical Areas (MSAs) over the period from 2004 to 2015, and apply a difference-in-differences approach. That essentially involves comparing MSAs with and without Uber, before and after Uber was introduced to each MSA. In addition to using a straightforward binary variable to capture Uber's presence (or not), they also use a measure of the intensity of Uber's penetration into each MSA market, based on the proportion of Google searches for "Uber".

While also controlling in their analysis for total employment and unemployment rates, population, and gas prices, Hall et al. find that:

...when Uber arrives in an MSA, transit ridership does not change much, with a coefficient that indicates there is a 0.26% increase in public transit use which is not statistically significant. However... as Uber becomes more commonly used in the MSA, there is an increase in public transit use, with a standard deviation increase in Uber penetration increasing public transit ridership by 1.4%.

In other words, Uber and public transport are complements. Hall et al. suggest that:

One reason Uber is a complement rather than a substitute for the average transit agency may be that transit is still much cheaper to use. The median minimum Uber fare is $5, while transit fares average just $1. Undiscounted fares for bus or light rail are never above $3, and for those with a monthly pass the marginal fare is zero. Transit is cheaper by enough that Uber’s role in adding flexibility to the transit system is more important than its ability to substitute for riding transit.

So, Uber is simply too expensive to be a substitute for public transport for most passengers. Hall et al. then extend their analysis, and find that:

Uber most strongly complements small transit agencies in large cities. This is likely because a small transit agency in a large city provides the least flexible service in terms of when and where they travel, and so Uber’s ability to add flexibility for such agencies is valuable to riders... In addition, transit riders in larger cities tend to be wealthier, and so there is greater overlap between those who ride transit and can afford to take Uber...

Finally, Hall et al. look at the effect of Uber on commuting times, using data from the American Community Survey. If passengers are using Uber for the 'last mile' portion of their journey, that may reduce their commuting time, but increase traffic and the commuting times for others. Hall et al. find that, as expected:

For public transportation users, the coefficients are large and negative, but the results are not statistically significant, and commute times for private vehicle commuters in large MSAs or those with a small transit agency increased by 1.5–2.5%. Together these results suggest that Uber reduced commute times for public transit users while increasing congestion.

So, in most cases, and especially in large cities with small transit agencies, Uber is a complement for public transport. However, adding Uber to a city is not without cost. Commuters who are not using public transport likely face more traffic congestion as a result of Uber (as I've noted before).

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Tim Harford on the network effects, switching costs, and the enshittification of apps

This week, my ECONS101 class has been covering pricing and business strategy. As we discuss in class, pricing strategy is essentially about creating and capturing value - the firm creates value for consumers (or other businesses), and then finds creative ways to capture that value back from the consumers (or businesses) as profits. So, it was timely to read this post by Tim Harford this week, looking at apps:

The writer and activist Cory Doctorow has coined a memorable term for this tendency for platforms to fall apart: enshittification. “Here is how platforms die,” he wrote in January. “First, they are good to their users; then they abuse their users to make things better for their business customers; finally, they abuse those business customers to claw back all the value for themselves.”...

Nevertheless, I’m quite sure enshittification is real. The basic idea was sketched out in economic literature in the 1980s, before the world wide web existed. Economic theorists lack Doctorow’s gift for a potent neologism, but they certainly understand how to make a formal model of a product going to the dogs.

There are two interrelated issues at play. The first is that internet platforms exhibit network effects: people use Facebook because their friends use Facebook; sellers use Amazon because it’s where the buyers are, while buyers use Amazon because it’s where the sellers are.

Second, people using these platforms experience switching costs if they wish to move from one to another. In the case of Twitter, the switching cost is the hassle of rebuilding your social graph using an alternative such as Mastodon, even if all the same people use it. In the case of Amazon, the switching cost includes saying goodbye to your digitally locked eBooks and audiobooks if you move over to a different provider. Doctorow is fascinated by the way these switching costs can be weaponised. His short story, Unauthorized Bread, describes a proprietorial toaster that only accepts bread from authorised bakers.

Both switching costs and network effects tend to lead to enshittification because platform providers see early adopters as an investment in future profits. Platforms run at a loss for years, subsidising consumers — and sometimes suppliers — in an effort to grow as quickly as possible. When switching costs are at play, the logic is that companies attract customers who they can later exploit. When network effects apply, companies are trying to attract customers because they will draw in others to be exploited. Either way, exploitation is the goal, and the profit-maximising playbook will recommend bargains followed by rip-offs.

All of this comes back to creating and capturing value. First, the firm uses its shiny new app to create value for consumers. The app can create a lot of value if the consumers can access it for free. That sucks the consumers into the network. A large network then creates value for advertisers, because it represents a large audience for their advertising. Finally, the firm can capture that value back from the advertisers in the form of profits. Although, as Harford's post notes, the process of capturing value back from advertisers reduces the value that consumers get from the app. However, since those consumers face switching costs to change to some other app, they are locked in to using the app. So, the firm is probably fairly unconcerned about the consumers' loss of value, so long as they continue to use the app (and create value for advertisers).

Just because pricing strategy is about creating and capturing value, that doesn't necessarily mean that firms are focused on creating value for consumers, if they can be more profitable by creating value for someone else, in this case advertisers.

Understanding lower domestic university enrolments

Stuff reported last week:

North Island universities are blaming high employment and a drop in school-leavers with University Entrance for a big fall in enrolments by New Zealand students.

All five North Island universities told RNZ they had started the year with fewer full-time-equivalent domestic students than the same time last year.

The drop was trivial at Waikato, but significant at the remaining four institutions.

The two South Island institutions that shared their figures with RNZ, Canterbury and Lincoln, reported increased domestic enrolments...

The University of Auckland said it had been expecting a fall in domestic enrolments this year, but the actual result was lower than expected due to "the relatively low numbers achieving University Entrance"...

Massey University described its eight percent drop in domestic FTE students as slight and said [it] was prone to labour market shifts...

Victoria University said its enrolments "have been affected over the past few years by the intersection of changes in student demand, the growth of new areas of study and the waning of others, demographic changes, unique economic conditions (i.e., low unemployment and growing cost of living), closed borders, and the broader impacts of Covid-19".

To understand what's going on, it is worthwhile to unpack the things that contribute to domestic school-leaver university enrolments. I focus on domestic school-leavers, as they make up the majority of new university enrolments, and it turns out that the changes are mostly predictable.

Take this simple identity:

E = P * [S/P] * [U/S] * [E/U]

E is the number of domestic school-leaver enrolments. P is the total population (of New Zealand). S/P is the proportion of the population who are school-leavers. U/S is the proportion of school-leavers who achieve university entrance. E/U is the proportion of those who achieve university entrance who ultimately enrol at university.

This identity essentially describes where domestic school-leaver enrolments come from, and understanding each component of the identity, and changes in each component, will give us a good understanding of what is happening with enrolments (and what is likely to happen in the future).

Let's start with P, the population of New Zealand. The population is growing, at about 0.7 percent per year. As for S/P, the proportion of the population who are school-leavers, this is shrinking slowly over time. This is obvious when you look at a population pyramid for New Zealand (like this one). The age cohorts at the bottom of the pyramid are smaller than those above them. That also means that when you combine P and S/P, the number of school leavers is declining, and likely to continue to decline in the future.

Moving on to U/S, the proportion of school-leavers who achieve university entrance, we know that this was artificially inflated during the pandemic, as NZQA gave away lots of bonus NCEA credits (e.g. see here for 2021). Those bonus credits came to an end this year, and there were fewer credits available in 2022. This means that we should expect a smaller proportion of school leavers to achieve university entrance from 2022, compared with 2021 or 2020. There may be a small decrease in 2023 again, and then beyond that the proportion of school leavers achieving university entrance will probably return to its long-run flat trend (see page 2 of this document).

Finally, E/U is the most uncertain component. The proportion of those who achieve university entrance who ultimately enrol at university depends on labour market conditions, as the statements from Massey University and Victoria University note. When the economy is doing well, and jobs for school leavers are relatively plentiful, more school leavers will opt to go straight into the workforce, rather than to university. Right now, at 3.4 percent, the unemployment rate is close to the lowest it has been over the last 15 years. Even the youth unemployment rate (for those aged 15 to 24 years) is very low, at 9.6 percent. However, the economy may tip into recession later this year (or at least, that is what is expected by many economists - see here and here). If the economy slows, there will be fewer jobs for school leavers (and for everyone else), and enrolments might start to pick up.

When we put this all together, we realise that the demographic trends (P and S/P) together contribute to lower enrolments, and student achievement (U/S) will lead to lower enrolments this year and next year (after a temporary increase over the last two years). Finally, economic conditions (E/U) will likely increase enrolments later this year, and perhaps into next year.

Overall, it probably isn't all bad news for universities as a whole in the short term, but only if the expected recession comes to pass. Longer term, the demographic changes weigh more heavily, and domestic enrolments are likely to decline over time as a result. Of course, the universities are busy competing for those declining enrolments, and that competition will no doubt heat up as the declining total enrolments start to eat into university budgets.

How not to strategise for penalty kicks

In game theory, a pure strategy is an unconditional choice of strategy for a player. In other words, the player chooses that strategy for sure. That distinguishes it from mixed strategy, where the player randomises their actions, choosing each of the possible strategies with some probability (which might be zero). There are lots of examples of mixed strategies. One that I use in my ECONS101 class is the choice for a tennis player over whether to serve down the middle, into the body, or out wide. If they chose one strategy for sure, they would reduce their chances of winning. Instead, they should randomise - sometimes choosing the first strategy, sometimes the second, and sometimes the third.

Another example from sports is the penalty kick in football (or soccer, if you prefer). The penalty taker must choose which side to kick towards, and the goalkeeper must choose which way to defend. I've discussed this game and the mixed strategy equilibrium before (see here and here).

The key problem with mixed strategy is that it genuinely involves randomisation. You cannot reason a pure strategy solution to a mixed strategy game. If you do, you end up with something like this:

I'm not sure where the video comes from (TV or movies, or something else), but it is very similar to a story related in the book Soccernomics, by Simon Kuper and Stefan Szymanski (as Robbie Butler notes here). The solution to mixed strategy games is not to try and solve them with pure strategy, but to randomise.

[HT: Jadrian Wooten at Critical Commons, via the Economics Media Library]

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