This week in research #128

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

• Ruggles tests Richard Easterlin's argument that the economic and social prospects of a generation are influenced by the size of the cohort relative to adjacent cohorts, and finds using US data from 1910 to 2040 that the theory fits the data well for the period from 1940 to 1980 but fails in later decades, although baby boomers exiting the labour force will likely lead to increases in wages in the future
• de Bondt and Sun (with ungated earlier version here) use ChatGPT to classify activity sentiment scores from Purchasing Managers’ Index (PMI) news releases, then use those scores to 'nowcast' GDP, finding that on average, out-of-sample forecast accuracy improves by about 20% apart from the two most recent years
• Skali et al. (open access) find that better-looking Swiss politicians are not more prone to rent-seeking through interest group affiliations, and do not deviate more from their voters' preferences
• Jin, Karim, and Schulze (open access) find that Islamist terror attacks created significant negative abnormal returns in American and European markets, but the stock market effects of other terror attacks were almost nil

In other news, I wrote a quick take on the New Zealand Budget as part of The Conversation's coverage this week. That article also has a drop-down menu at the bottom that summarises the key Budget announcements in each area

Try this: Taxed

Today was Budget Day in New Zealand. The government revealed its forecasts of future revenue and its spending plans. There is a good summary of this on The Conversation (disclaimer: I wrote the blurb at the top of that summary).

The problem with the Budget is that the numbers are large, and it is difficult to get a good sense of the relative magnitudes. How do you interpret $1.18 billion in spending on rail network renewal and upgrades?

One of my recent students, Tyler Dunseath, created the Taxed website, that uses your income to work out how much tax you pay (weekly, fortnightly, monthly, or annually), then apportions that tax to the various categories of spending from the government accounts. So, for example, if your weekly income is $1000 before tax, and you don't adjust for ACC, KiwiSaver, or student loan repayments, you pay $165.77 in tax. Of that, $56.79 goes to social security and welfare, $37.40 goes to health, $24.25 goes to education, and so on. The results give you a better sense of how taxes are distributed.

Of course, there are a number of caveats, the biggest of which is that government services are a bundle, and while Taxed might make it seem like you could in theory say, "I don't want to pay $0.32 per week for international peacekeeping", it doesn't work that way. Moreover, a lot of government spending is on services that are public goods and therefore non-excludable, so even if you could opt out of paying for them, you would still receive the benefits of them.

Second, government receives some income that is earmarked for particular purposes. For example, the fuel excise tax is earmarked for the National Land Transport Fund. So, your income tax isn't distributed in exact proportion to the government's spending on different categories, because less of your income tax goes towards transport.

Third, the site doesn't account for the taxes we pay on goods and services (GST, or excise taxes on alcohol, tobacco, or fuel), or the user charges we pay.

With those caveats in mind though, Taxed is a pretty cool way of showing how the government's spending is distributed, and in a way that most people are more likely to understand than the millions or billions of dollars cited in the budget.

Enjoy!

[HT: Tyler Dunseath]

Is it better to have a more educated mayor?

It seems somewhat self-evident that having a more educated mayor would be better than having a less educated mayor. However, whether education is a positive attribute for a mayor really depends on whether, and to what extent, more educated mayors act differently than less educated mayors. Do they spend more, or less? How do they spend the public budget?

This new article by Alessio Mitra (University of Kent), published in the European Journal of Political Economy (ungated earlier version here) directly addresses the second question - how does mayoral education affect public finance? Mitra uses data from municipal elections in Italy over the period from 2000 to 2015, focusing on municipalities with a population of less than 15,000 (because larger municipalities use different electoral rules). He defines a more educated mayoral candidate as one with a university degree, and a less educated mayoral candidate as one without a degree.

Mitra applies a regression discontinuity design (RDD), which involves comparing municipalities that narrowly elected a more educated mayoral candidate over a less educated candidate with similar municipalities where the more educated candidate lost to the less educated candidate. In very close elections, the identity of the winner is plausibly as-good-as random, provided there is no manipulation around the threshold related to the education of the candidates. In other words, since the difference between getting 50.01 percent of the vote and getting 49.99 percent of the vote is essentially random, the education of the winning mayoral candidate is basically determined randomly in these close elections between candidates with different education levels. With that assumption in mind, observed differences between the municipalities where a more educated candidate won with those where they lost can be attributed to the difference in mayoral education.

Mitra's dataset includes more than 18,000 mayoral elections, of which 1211 have a margin of victory of less than five percent (which he defines as a close election, and includes in the analysis). He looks at the differences in public expenditure, initially focusing on changes in the share of spending devoted to operational expenses (or 'current expenditure' as he terms it) or public investment. In this, Mitra finds that:

When an educated mayor is elected by chance, public investment rises by 3 percentage points of total expenditure compared to a less educated counterpart.

Digging down into the allocation of that public investment, he finds that:

...educated mayors allocate an additional 1 percentage point of total expenditure to education investment, accounting for one-third of the overall increase in public investment.

And going a bit deeper than that:

Among education investments, immovable assets dedicated to nurseries receive the largest increase in resources.

Consistent with Italy’s balanced budget requirement on municipalities, there is no significant change in fiscal deficit. That means that the additional spending devoted to public investment must mean a corresponding reduction in operational expenditure. Mitra doesn't really dig into that at all.

What we take away from this paper is that more educated mayors devote more spending to education. In the Waikato Economics Discussion Group today, we discussed what mechanisms might underlie this difference, which is something that Mitra didn't explore. Perhaps more educated mayors see more value in education. After all, they invested more in their own education than a less educated mayor did. However, that's not entirely consistent with spending more on public investment in early childhood education.

A second possibility is that more educated mayors have a lower intrinsic discount rate, increasing their willingness to make long-term investments, both in their own education and in the education of their citizens. This is more consistent with devoting spending to public investment in early childhood education.

A third possibility is that more educated mayors may be better at the administration of public investment, such as project approvals, capital budgeting, grants, or procurement. This means that they have greater capacity for public investment projects. However, that greater capacity wouldn't necessarily be more apparent for public investment in education, or early childhood investment.

However, an intriguing but speculative fourth possibility is that more educated mayors understand that public investment can be used strategically to affect demographics. Many municipalities in Italy are facing extreme population ageing and/or declining populations. Mitigating (but probably not reversing) those population changes may be possible through creative policy. If the municipality invests in early childhood education, that may make the municipality more attractive for young parents to relocate to, and may reduce cost pressures that hamper fertility. The problem with this as an explanation is that it isn't clear that these trends and policy solution would be more apparent to a more educated mayor than to a less educated one.

The second possibility seems to me like the most promising. However, exploring the reasons why more educated mayors spend more on public investment, particularly in education, is a promising exercise for future research.

One last point is that the effects are actually quite modest. The total budget for a municipality of 15,000 population would be around €15-25 million per year (based in part on this and this, both in Italian, but see also here for public finance data for all Italian municipalities). A reallocation of three percentage points to public investment represents up to an additional €750,000 per year. And if one-third of that is spent on public investment in education, that is an additional €250,000 per year. It's not nothing, but it's certainly not building multiple new schools. Maybe it's an additional small school building per year.

So, is it better to have a more educated mayor? This research suggests yes, but that relies on a normative view that more spending on public investment, particularly in education, is overall a good thing. However, the size of the effect doesn't suggest transformational change, and we don't really know what the trade-offs are in terms of what categories of operational spending were reduced. A university degree does not necessarily make someone a better mayor, and this paper cannot tell us whether more educated mayors have better preferences, longer time horizons, or simply greater administrative capacity. What it does show is that who gets elected can change not just how much is spent, but what kind of future a municipality chooses to invest in.

Does the future of higher education look more like a mentoring pyramid scheme?

In response to my recent post about the future of higher education and one-on-one mentoring, one of my students from last year, Yunze, got in touch via email to offer a potential solution:

...I wonder whether it is possible to set a clear academic threshold within each discipline. If students who reach this threshold could mentor upper‑middle‑level students, while professors spend only a small amount of time supervising the overall direction, the system might become more sustainable. However, I suspect this could harm the interests of the top students, since they might otherwise use that time to further advance their own academic achievements, and If [sic] they fail to successfully train students with real research ability, it would likely damage both the university’s reputation and the professor’s own reputation.

You know, I think Yunze is right on the money here. Consider the problems I outlined in the earlier post: (1) the signalling value of education is falling due to generative AI; (2) a one-on-one mentoring approach may be a solution; but (3) one-on-one mentoring doesn't scale due to limited faculty time. If one-on-one mentoring is not conducted between faculty and students, but works more like a pyramid mentoring model, then this might actually work, not just for students, but for faculty and for universities as well.

So, let's think it through. But first, remember that the mentoring model I introduced in the earlier post is not simply a model of small classes, where senior students perform limited teaching roles, such as tutoring. This is a model of genuine mentoring, where the mentor encourages the mentee to become a builder, in the words of Auren Hoffman. A builder creates things, and it is the act of building, and the learning alongside that, which will be a durable signal to future employers. In relation to mentoring, I said in that post that mentors should do the following for their mentees:

Teach them to be builders. Encourage them to create things. Work with them and chart a path forward for their success.

If faculty provide one-on-one mentoring to a small number of senior students, then that makes better use of faculty time than them mentoring hundreds of first-year students. The senior students can then each mentor several second-year students, who in turn can then mentor several first-year students. [*] In this model, faculty time is targeted at the senior students, where the impact of faculty on student employment outcomes may be greatest.

Students benefit from helping junior colleagues to become builders, where the signalling value may remain even in the face of generative AI. Even better, mentoring provides student mentors with an opportunity to build - they may be able to point employers to the success of their mentees as an example of their building, talking also about what went wrong in the mentoring relationship, and what they learned from the experience.

In this mentoring pyramid model, universities retain a key role, but that role becomes very different. Universities essentially become a platform, connecting students with mentors - first-year students with second-year mentors, second-year students with senior student mentors, and senior students with faculty mentors. In the terms of my earlier post, the university runs their own OnlyStudents platform.

Of course, this platform role creates a new problem for universities. If mentoring works mainly as a way of matching students with mentors, then the market may not need eight OnlyStudents platforms in New Zealand, or thousands of OnlyStudents platforms worldwide. A small number of large platforms could have a big advantage in that case - more students attract more mentors, more mentors improve the quality of matching, and better matching attracts still more students. Those network effects could create a winner-take-all dynamic, in which universities would struggle to differentiate themselves simply by running their own mentoring platforms, and where a single surviving OnlyStudents platform might be the ultimate outcome. However, that conclusion depends on the strength of the network effects. If effective mentoring also depends on institutional trust, disciplinary reputation, local employer connections, pastoral care, or an in-person community, then universities may retain some defensible advantages. Geography alone probably won’t be enough, especially if online mentoring is close to being as effective as in-person mentoring, but local connections might still matter. So the question for universities is not just whether they can build their own OnlyStudents, but whether they can attach that platform to something that a larger, more generic OnlyStudents cannot easily replicate.

Universities may also retain a role in the initial and ongoing training of mentors. Since each student, and each faculty member, will need to be a mentor to one or more others lower down in the pyramid, they will need to understand how to mentor. That means universities would not simply be matching students with mentors. They would also need to train mentors, monitor the quality of mentoring relationships, and intervene when mentor-mentee relationships are not working well. Moreover, the adoption of a mentoring pyramid model is likely going to change who the most successful students (and faculty members) are. The top students do not necessarily make the best mentors (or the best tutors, as I have learnt across years of coordinating tutors in my first-year papers). Good mentoring requires a specific skill set, but it is those skills that may also demonstrate the quality of the student as a builder - a signal of high quality for employers.

A further point about the pyramid mentoring model is that it likely requires a strong filtering effect to be financially viable. Since each faculty member can only mentor a limited number of senior students, and each of those senior students can only mentor a limited number of second-year students, who in turn can only mentor a limited number of first-year students, each level of the pyramid probably needs to be somewhat wider than the levels above it. To achieve that, student progression needs a strong filter, limiting the number of students who progress from first-year to second-year, and from second-year to senior.

Let's consider some simple numerical examples that illustrate why filtering is needed. If each faculty member mentors five senior students, and each senior student mentors five second-year students, who each mentor five first-year students, then the pyramid contains 155 students per faculty member - five senior students, 25 second-year students, and 125 first-year students. A model where each faculty member’s salary is covered by fees from 155 students, setting aside any contribution to central university costs, seems likely to be financially viable to me. However, in this model only one-fifth of students could be allowed to progress each year. That means the model would also need some form of orderly exit for students who are filtered out - perhaps an exit qualification, or a pathway into a non-mentored track. The problem is that both options may provide negative signals about the student who is filtered out.

If all students were to progress, then that would require each student to mentor at most one student at the level below. Keeping five senior students mentored by faculty, then the pyramid would contain 15 students per faculty member - five senior students, five second-year students, and five first-year students. That system would be much less likely to cover the cost of faculty time. So, it's unlikely that the pyramid mentoring model would be viable to run without some form of filtering - perhaps not as extreme as only one-fifth of students progressing each year, but clearly not all students could progress every year.

So, to return to my conclusion from the previous post, the current mass higher education model still looks increasingly fragile, but perhaps one or a few universities might be able to navigate their way through. However, the survivors are likely to be first-movers or fast followers in developing a platform market strategy that leverages a pyramid mentoring model. This model is still going to cost students a lot, and the filtering effect would make higher education more elitist as well.

And thanks to Yunze for inspiring this post with his perceptive email comments.

*****

[*] For simplicity, I'm assuming a three-year higher education degree structure, as we have in New Zealand. For a four-year degree structure, you would of course need to add an additional level.

Read more:

• Why all students need to understand adverse selection and signalling
• Signalling theory suggests that we may need two categories of university degree
• Does the future of higher education look more like one-on-one mentoring?