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?

Book review: The Corporation

I just finished reading Joel Bakan's 2004 book The Corporation. The book (and the accompanying documentary film (which is available on YouTube) outline the pathology of the corporation, given the centrality of corporations to modern life. On that point, Bakan writes that:

Today, corporations govern our lives. They determine what we eat, what we watch, what we wear, where we work, and what we do. We are inescapably surrounded by their culture, iconography, and ideology. And, like the church and the monarchy in other times, they posture as infallible and omnipotent, glorifying themselves in imposing buildings and elaborate displays. Increasingly, corporations dictate the decisions of their supposed overseers in government and control domains of society once firmly embedded within the public sphere.

This is Bakan at his most sweeping, and not always at his most convincing. It's clear that we have far more agency in our dealings with corporations than Bakan intimates. But nevertheless, corporations are and have been a big influence on consumer and government decision-making over many decades. That is why, despite being over twenty years old, the book retains its currency. The underlying incentive problems for large corporations have not really changed in that time, even if the particular corporations that might be the target of criticism may have.

Bakan's book develops a portrait of the corporation and its influence that is more than a caricature based on worn-out tropes. His work is based on exhaustive interviews (many of which can be seen in the documentary film) and research. And Bakan avoids the temptation, which many authors of similar books seem not to, to pin the blame solely on neoliberalism, laissez-faire economics, or economics in general. Instead of relying on such lazy cliches, Bakan provides a reasoned argument for questioning the place of the corporation in the modern economy.

One part of the book I appreciated in particular was the section talking about corporate social responsibility (CSR). I hadn't appreciated that CSR initiatives dated back as far as the end of World War I, where they were referred to as 'New Capitalism'. Bakan really takes issue with the double standards that corporations play, which is ably demonstrated by this passage:

Take the large and well-known energy company that once was a paragon of social responsibility and corporate philanthropy. Each year the company produced a Corporate Responsibility Annual Report; the most recent one, unfortunately its last, vowed to cut greenhouse-gas emissions and support multilateral agreements to help stop climate change. The company pledged further to put human rights, the environment, health and safety issues, biodiversity, indigenous rights, and transparency at the core of its business operations, and it created a well-staffed corporate social responsibility task force to monitor and implement its social responsibility programs... The company, which was consistently ranked as one of the best places to work in America, strongly promoted diversity in the workplace. "We believe," said the report, "that corporate leadership should set the example for community service."

That corporation was... Enron. Bakan delivers the punchline flawlessly.

This book is also more than a simple polemic against the evils of corporations. Bakan also considers potential solutions that could bring corporate behaviour more in line with social goals, and with more sincerity and depth than Enron clearly displayed. Bakan considers the potential for regulation, but is skeptical about it due to the risks of regulatory capture. In that, he rightly refers to the work of the economist George Stigler. Bakan finally alights on the prospects of charter revocation laws - laws that would allow the government to terminate a corporation. This is, obviously, a far greater penalty than has ever been imposed on a large corporation. Nevertheless, the rules do exist in many countries. 

Bakan's focus on charter revocation is interesting, but seems like an disproportionate response. If we consider the corporation as a person, which is a position that Bakan explicitly critiques, then charter revocation is the equivalent of a death sentence. While some may think that the worst actions of corporations deserve a penalty at that end, the innocent shareholders of the corporation would no doubt disagree. Aside from charter revocation, Bakan also notes that there are several things that governments can do, including improving the regulatory system, strengthening political democracy, creating a robust public sphere, and challenging international neoliberalism. Alongside that, greater penalties for senior managers and directors of corporations that break the rules would likely be an improvement, since that would focus more directly on changing the incentives of the decision-makers whose actions lead to corporate wrongdoing.

I enjoyed reading this book, especially as I didn't feel the need to be constantly defending economics in my mind while reading it. Sadly, Bakan's book could have been written today as many of the problems that he outlines are just as apparent with corporations in the 2020s as they were in the 2000s. There has been plenty of CSR language since then, as well as the rise of environmental, social, and governance (ESG) initiatives, but it is hard to see much of this as more than the same low-level box-ticking exercise that Bakan critiques. There has been far less movement on the deeper institutional reforms that Bakan favours, so it is likely that the problems will be just as apparent for corporations in the 2040s. It doesn't have to be that way, and anyone who believes in change would do well to read this book as a starting point.

Does the future of higher education look more like one-on-one mentoring?

It almost seems a cliche to say that generative AI is both the greatest threat, and the greatest opportunity, for higher education. That doesn't make the statement any less true. And there are many commentators who are trying to work out what happens next for higher education. I think one of the best is Hollis Robbins, whose Substack Anecdotal Value is well worth subscribing to.

Robbins was recently interviewed by Jay Caspian Kang of The New Yorker (paywalled, but you can find an ungated version here) about the future of higher education. The whole interview is worth reading, but I want to highlight this bit in particular, where Robbins says:

I was in Austin, Texas, a couple of times in March with a bunch of twenty-five-year-old billionaires. This is what they’re looking at. Instead of having the credential from the institution, why not have the credential from the professor? If you have a Hollis Robbins education, what would that signal? What would that credential mean as opposed to a degree from a university? There was some conversation about what that would look like, and one guy at the end of the dinner said, “Instead of OnlyFans, it’s like OnlyProfessors.”

The correct analogy here would be that it would be OnlyStudents, not OnlyProfessors (since the name contains the audience, not the performer). However, Robbins makes a good point. Higher education is, in part, an exercise in signalling (as I've noted before here and here). In fact, Bryan Caplan argued in his book The Case Against Education (which I reviewed here) that one third of the benefit of higher education is signalling (the other two-thirds is made up of genuine learning, socialisation, and transferable skills). 

The diploma that a student receives at the end of their higher education journey is a signal to employers of the student's quality as a future employee. The signal is credible to the employer because it is costly for the student to obtain, and costly in such a way that low-quality students wouldn't attempt the signal. University reputation matters here, because it is an assurance of the second of those conditions - low-quality students wouldn't attempt the signal of a Harvard degree, firstly because they wouldn't gain admission to Harvard in the first place. So, part of the signal comes from getting into Harvard. Second, low-quality students wouldn't attempt the signal of a Harvard degree because passing courses at Harvard is hard (or, at least, harder than at many other universities). The quality of the education at Harvard has traditionally been higher than elsewhere.

In the interview, Robbins makes a further important point, which is that we've spent the last several decades making higher education a commodity. Students studying a degree in a particular subject learn the same things, often using the same teaching materials, the same textbook, and the same style of assessment, regardless of which university they go to. That means that the quality of signal that arises from the education itself has reduced over time, meaning that most of the value of the signal arises from the admission process. Once a student has been admitted to Harvard, their signal is in place, and the further signalling from their education is lower than for comparable students in years past.

Robbins argues that generative AI is accelerating and expanding this commodification of education. Since generative AI has access, through its training corpus, to a large store of human knowledge, to add value over and above generative AI a professor must be a true expert in a very specific subfield. For students, learning a subject from a true expert still retains value, because generative AI cannot as easily replicate the learning that would occur from the expert. At that point, the university becomes less important as a mediator of education.

In other words, Robbins is arguing that the university could be disintermediated, with individual professors issuing credentials instead of universities. Who needs a Harvard degree, when you could have a Hollis Robbins degree? And since Robbins is an expert (in African American sonnet tradition, she says), the signal retains high value. However, that is true only to the extent that employers find the Hollis Robbins degree a credible signal.

That brings me to this Substack post by Auren Hoffman. Hoffman also argues that generative AI has diminished the value of the higher education signal. However, Hoffman argues for a different solution, this time from the perspective of the graduating student:

you have to show you can add value. that is it. that is the only thing.

the test the smart hiring manager applies in 2026 is simple. can you learn something on your own? can you finish what you started? can you do what you said you would do? these were always the skills that mattered. the difference is they are now the ONLY skills that matter, because the credential stopped doing the screening.

if you have a few years of experience, your resume can show you have these skills. if you are a new grad, you have to show what you have created and built.

Hoffman is overstating things a little, as university degrees are unlikely to disappear overnight. However, his critique is still important, as is the implication he draws. Hoffman argues that graduates (or young people, generally, since the solution doesn't depend on a student attending a university or completing a degree) need to become builders:

the most valuable thing a 22 year old can do in 2026 is create something. an app. a screenplay. a side business. an internal tool you wrote for a club you were in. a dinner series. a script that automates something annoying. a website. a chrome extension. a sculpture. a dance party. a discord bot. a substack with 32 readers and a real point of view. anything that moved from idea to working.

will the thing make money? probably not. that is not the point.

the point is that you taught yourself something. you finished it. you can describe what you learned, what broke, what you fixed, why you made the calls you made. that story is the new resume.

every hiring manager would rather interview a 22 year old with a launched app and a github full of weird side projects than a 22 year old with a 3.9 GPA from a top 50 school. it is not close. when one candidate has tangible evidence of what they can ship and the other has a transcript, the transcript will lose every time.

According to Hoffman, the best signal for young people to be sending in future is that they can build. Our students will ultimately be more successful if they can show off their skills (both technical and transferable skills) by building something. That is a signal that is costly, and costly in such a way that low-quality students will not attempt it. The signal is credible, and for the most part it retains currency even in the face of generative AI. Indeed, if the student builds something while effectively leveraging generative AI, then the signalling value to employers may be even greater. The key distinction is between the student using generative AI as a tool and the student using it as a substitute for doing the work. A student who can explain what they built, what broke, what they learned, and why they made the choices they made, is still sending a costly signal. A student who simply lets generative AI build for them is not. Employers would likely see through the latter pretty quickly.

Hoffman's idea isn't exactly new. When I think about some of my best students over the past two decades, they tend to be those that built something either while they were studying, or immediately after. For some, this was their own small business or entrepreneurial activity. For others, it was writing and publishing a research paper. Those were challenging tasks that set them apart from other students - a clear signal of quality. What Hoffman is essentially saying is that, with the signal from higher education itself being removed, the only remaining signal of quality is the signal from being a builder.

Where does that leave higher education staff? I think we can combine Robbins's and Hoffman's ideas, and chart a path forward. We don't need to start an OnlyStudents, and issue our own degrees, but we do need to cultivate closer relationships with our best students. Teach them to be builders. Encourage them to create things. Work with them and chart a path forward for their success. In other words, be a mentor.

Universities are absolutely going to hate this. Mentoring is not an activity that can be offered at scale. For example, there are simply not enough hours in the day for me to individually mentor all of the 350-plus students in my first-year economics class. Nor is mentoring easy to timetable, measure, standardise, or reward under current academic workload models. Universities are built around papers, credit points, learning outcomes, assessment rubrics, and student evaluations, all of which can be offered at scale. One-on-one mentoring doesn't work so well in that system. For example, postgraduate supervision already sits awkwardly within the system, with it being unclear whether it counts as teaching, or research. Nevertheless, mentoring may be one of the few ways that higher education can continue to offer something that is both valuable and difficult for generative AI to replicate.

If generative AI significantly reduces the signalling value of university education, and if students increasingly use it to avoid genuine learning, then the current mass higher education model looks increasingly fragile. Moreover, what remains is going to cost students a lot more. If having a high-quality mentor who can encourage a student to build is the path to their future employment, then it may be worth it. After all, high-quality signals are costly. That aspect of education, at least, won't have changed.

[HT: Marginal Revolution, for both the Hollis Robbins interview and the Auren Hoffman post]

Read more:

• Why all students need to understand adverse selection and signalling
• Signalling theory suggests that we may need two categories of university degree

This week in research #127

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

• Ertl, Holb, and Bakó (with ungated version here) find, using a field experiment, that using a loss-framing in higher education assessments improves academic performance throughout the semester and on final assessments
• Donadelli, Mammi, and Paradiso (open access) investigate the impact of major wars and pandemics on supply and demand dynamics in G7 economies since the 1800s, and find that they primarily and persistently damage the supply side of the economy
• Bouchard, Yun, and Shelton (with ungated earlier version here) find that a 20-percentage point increase in the Trump two-party vote share correlates with an 8 percent increase in post-election domestic violence, following Trump's 2020 Presidential election loss

The American Economic Association papers and proceedings were published this week, which included the following papers of interest:

• Mayer, Méjean, and Thoenig find that a sufficiently strong and targeted set of sanctions, such as the 2024 sanctions imposed on Russia, would have increased Russia’s opportunity cost of war enough to deter a full-blown conflict in Ukraine
• Aksoy et al. find that younger CEOs, and younger firms, are more likely to allow their employees to work from home
• Li and Xu find that condensing a community college course into a shorter format improves course persistence, performance, and subsequent course outcomes in in-person settings, but not in online settings
• El Khoury confirms that female professors receive systematically lower teaching quality ratings than male professors, and that there is little difference in this by gender or minority status, but students who have ever posted a rating of a professor on a website penalise female professors by more than those who have not
• Underwood, Marshall, and Bilen find that redesignating economics as a STEM subject significantly increases the number of economics degrees conferred to women and non-resident students
• Ahlstrom, Asarta, and Harter investigate the use of AI in undergraduate economics courses, and find that there is a range of views about AI, but overall more experienced instructors are less inclined to integrate AI into their teaching or to adopt an open stance toward student use of AI tools to complete assignments
• Owen, Skoy, and Zhan find that easy-to-implement interventions can increase the probability that female students take additional economics courses beyond the introductory class, but that additional interventions do not have an incremental effect
• Orlov and McKee find that concurrent supplemental courses (on essential maths skills) can meaningfully improve outcomes for students at risk of underperforming in introductory microeconomics

Edmund Phelps, 1933-2026

Economics has recently lost another of the greats, Nobel Prize-winning economist Edmund Phelps, who passed away last week. Phelps was a macroeconomist, and among his many contributions he helped to formalise the concept of the natural rate of unemployment. Phelps won the Nobel Prize in 2006 for "his analysis of intertemporal tradeoffs in macroeconomic policy".

Surprisingly, for a Nobel Prize winner there have been few obituaries online so far. However, the New York Times has a good one (albeit paywalled). Phelps was 92, and had been among the oldest living economics Nobel laureates. Vernon Smith (born in 1927, so 99 years old), and Robert Aumann (born in 1930, so 95 years old) are the only economics Nobel Prize winners who are older.

The inter-temporal trade-offs that Phelps won his Nobel Prize for were not the only trade-offs that he considered in his research. Phelps also had a lot to say about one of the most famous trade-offs in macroeconomics - the short-run trade-off between inflation and unemployment that is embodied in the Phillips Curve (named after the New Zealand economist Bill Phillips). Phelps provided a keen critique of the short-run Phillips Curve, in particular noting that policymakers could not exploit the relationship permanently and, for example, 'buy' lower unemployment at a cost of higher inflation. He argued (successfully) for greater consideration of the role of inflation expectations in the relationship between inflation and unemployment (which my ECONS101 students will encounter next week). This critique contributed to our understanding that the Phillips Curve is vertical in the long run.

Phelps also contributed the 'Golden Rule' saving rate in the Solow growth model, which is the saving rate that maximises sustainable consumption per capita in the long run, as well as some of the key ideas underlying statistical discrimination (for more on that, see this post, or this one).

Phelps’s passing is a reminder that many of the ideas we now teach as standard macroeconomics were derived from hard-won arguments about expectations and the limits of policy.

My 18-month detour through second-degree price discrimination terminology

When I was composing this post about price discrimination last month, I was drawn into a discussion with ChatGPT about second-degree price discrimination. ChatGPT, which I mostly use for checking for inconsistencies and grammatical errors in my draft blog posts, told me that I should refer to menu pricing as a form of second-degree price discrimination. I replied that wasn't correct, because second-degree price discrimination, as defined by Arthur Pigou in the early 1920s, involves offering a declining price for each additional unit that the consumer buys. ChatGPT responded that indeed, Pigou had defined second-degree price discrimination that way, but that in much current industrial organisation usage, second-degree price discrimination includes cases where consumers are offered different options and sort themselves into groups that have different price elasticities of demand (or different willingness to pay) for the good.

That discussion made clear that I had been on an 18-month detour in how I described the degrees of price discrimination. Only last year, I changed the definitions of the degrees of price discrimination in my ECONS101 class to match those that Pigou uses, and therefore moved menu pricing into the definition of third-degree price discrimination (or group pricing). I've held off on posting about my exchange with ChatGPT until now, because I didn't want to confuse my students in this trimester's class about what did, and did not, fall under the different degrees of price discrimination before they were tested on it (and, as it turns out, I didn't test them on that specific aspect of the topic in any case). [*]

This appears to be one of those situations where terminology changes meaning over time, and is a cautionary lesson in making sudden changes to definitions on the basis of reading about the history of economic thought. The issue here is that I had come across Pigou's definitions in one source, and initially dismissed it as it was inconsistent with the way we taught that topic. But then I read The Economics Book by Niall Kishtainy and co-authors (which I reviewed here), which made me more certain about Pigou's definitions. To be clear, I'm not blaming Kishtainy et al. They were perfectly correct in terms of Pigou's definitions. I should have checked some other sources for more current usage. One example is the excellent book Information Rules, by Carl Shapiro and Hal Varian (which I read in 2023 and reviewed here), which made the definitions used in industrial organisation clear (although Shapiro and Varian preferred to use the term 'versioning', rather than second-degree price discrimination).

Now I'm left with the task of combing through my past posts, to ensure that I update my terminology, or revert it to the original text in the few cases where I went back and made changes. I don't want to risk confusing future students, which is a risk given that I refer them to my posts for further detail and examples on topics that we discuss in class.

*****

[*] I didn't perfectly achieve this goal, because one very alert student picked up the error through her own conversations with Harriet, our ECONS101 AI tutor, an irony that was not lost on me.

Exposure to conflict and support for democracy in Africa

Does being exposed to conflict, whether violent conflict or protests, increase or decrease people's support for democracy? That is the question that is addressed in this new article by Nicole Stoelinga (Max Planck Institute for Behavioral Economics) and Tuuli Tähtinen (University of Munich), published in the journal World Development (open access).

Stoelinga and Tähtinen have a quite innovative approach to identifying the answer to that question. They exploit the timing of data collection from the Afrobarometer survey, comparing research participants who were surveyed in the two weeks before a conflict event in their region with research participants who were surveyed in the two weeks after the event, in terms of their support for democracy and perceptions of the extent of democracy in their country. Controlling for other characteristics of the research participants, in theory any difference in support for democracy or perceived extent of democracy between those surveyed before, and those surveyed after, the conflict event should arise as a result of the conflict. This of course relies on survey timing around the conflict event being 'as good as random' (more on that later).

Their data covers up to 38 countries (the exact number varies across waves of the Afrobarometer survey) over the period from 2002 to 2021. Their conflict data comes from ACLED, and distinguishes between 'violent conflict events', which include "battles, explosions/remote violence, and violence against civilians", and 'demonstrations', which include protests and riots. Their dataset includes over 24,000 observations for violent conflict events, and over 42,000 observations for demonstrations. In their main results, they find that:

...conflict has a positive impact on support for democracy. The point estimates indicate that on average, a conflict event in one’s region increases the probability of support for democracy over other forms of governance by 2 percentage points. Exposure to violent events and exposure to protests have very similar impacts. In contrast, individuals’ perceptions of governance in their own country are not significantly affected by conflict exposure.

That seems to me to be a quite surprising result - exposure to conflict increases the support for democracy. Stoelinga and Tähtinen then dig a little deeper, looking at the difference in results between members of an ethnic in-group (which is an ethnic group that is 'represented in government', meaning that it governs alone or shares power), and members of an ethnic out-group. In this analysis, they find that:

The point estimates are imprecisely estimated, but suggest that exposure to violence increases support for democracy among out-group members, while the point estimate of similar magnitude and opposite sign suggests no effect among in-group members.

When they say that the estimates are 'imprecisely estimated', they mean that they are statistically insignificant. However, in terms of perceptions of democracy in the country, Stoelinga and Tähtinen find that:

...exposure to violence has a significant positive impact on individuals’ perceptions of governance in their own country.

That result holds for both the in-group and the out-group. Demonstrations have no effect on perceptions of democracy for either group. Stoelinga and Tähtinen interpret these results as showing a 'rally around the flag' effect, where people exposed to violent conflict show greater support for their leaders. Stoelinga and Tähtinen provide further evidence in support of this 'rally around the flag' effect by looking at trust in institutions, finding that:

...exposure to violent events significantly increases trust in president and ruling party, as well as in the police and army—but significantly less for the in-group than for the out-group.

Stoelinga and Tähtinen then turn to looking at differences in results between more autocratic and more democratic regimes, finding that:

In autocracies, exposure to either violence or protests increases both support for democracy and the perceived extent of democracy. Conversely, in democratic countries, conflict exposure does not have a significant impact on democratic preferences, although the point estimates suggest that the perceived extent of democracy is negatively affected...

In autocracies, exposure to violence generally increases trust in key political institutions, particularly in president and ruling party, as well as in the army... In democracies, the effects of conflict exposure on trust are less pronounced. Although mostly lacking statistical significance, the estimates point towards both violence and protests having negative effects on trust.

In other words, the 'rally around the flag' effect is a feature of autocracies and not of democracies. That should worry advocates for democracy who might hope that popular movements, which may be characterised by protests or riots, and may occasionally escalate into violent conflict events, might lead to regime change. Instead, the results in this paper suggest that conflict events increase support for democracy, but also increase trust in autocratic governments, and so may be counterproductive. Stoelinga and Tähtinen suggest that this may explain the lack of democratic transitions in Africa. To that, I would add that a similar mechanism may also explain Tunisia slipping back towards authoritarianism in recent years, despite the Arab Spring uprising.

Now, it should be noted that these results could be sensitive to the research design. If conflict events change the way that data collection activities are conducted, or change the locations of data collection, then that would confound the effects of conflict on support for democracy. For instance, if the Afrobarometer survey was originally scheduled for Region A, but that region is affected by conflict and instead they shift to Region B, which is less affected, that might bias the results in exactly the direction that Stoelinga and Tähtinen observe - towards more positive views towards democracy, if those who are less affected by conflict have greater support for democracy. Stoelinga and Tähtinen are upfront about the potential invalidity of their results arising from endogenous change in the timing or location of survey data collection. However, they argue that, because there are no observable differences in research participants before and after the conflict event, there is no problem. That doesn't address whether there are unobservable differences between research participants before and after the conflict event though.

Another issue with the research design is that conflict events don't suddenly arise out of nowhere, so the 'control group' of research participants surveyed before the conflict event may already have experienced some of the antecedents to the conflict event, such as growing tensions and uncertainty. That would have an ambiguous impact on the results of this study.

So, we should not treat these results as the last word on this research question. The paper provides a surprising and counterintuitive finding, that conflict may increase support for democracy, but in autocracies it may also strengthen trust in government institutions. That complicates the appealing idea that protest or conflict will naturally push autocratic countries in Africa towards democratic transition.

The modest impact of Australia's baby bonus on fertility timing

The challenge of returning low-fertility countries to a higher-fertility state has become especially clear in recent years. As noted in this post, aside from the post-WWII Baby Boom, there have been no significant episodes of increasing fertility. And that's not for want of trying. Some governments have become increasingly generous over time in their attempts to encourage higher fertility. Others have flailed around looking for a solution. One notable example is the Australian 'baby bonus', which initially paid each mother a lump sum of $3000 for each child born after 30 June 2004. The amount was increased to $4000 in July 2006, then to $5000 in July 2008, before being reduced to $3000, and eventually removed (and replaced with changes to the Family Tax Benefit) in March 2014.

How (un)successful have policies like Australia's baby bonus been? This new article by Sarah Sinclair (RMIT University) and co-authors, published in the journal Economic Modelling (open access), takes an unusual approach to answering that question. Rather than identifying policies and then testing directly for whether fertility changes happened at those points in time, Sinclair et al. first use time series models to identify structural breaks in the time series of fertility for 31 maternal-age-by-birth-order series. A structural break occurs when the time trend for the series changes meaningfully at a particular point in time. Using their approach, Sinclair et al. look for points in time where many of the time series have meaningful changes. What they find is not much of anything, with:

...the clearest and most consistently identified turning points for second births, with breaks in 2005 and 2015 detected across dates that plausibly align with major changes in family transfer settings. Other shifts, such as in selected age groups and some higher-order births, are less robust, and we detect no structural break in the aggregate fertility rate.

The timing of the 2005 and 2015 changes is consistent with the timing of the major changes to the baby bonus (or, at least, consistent with nine months after the major changes to the baby bonus). However, notice that they found effects only for second births, and not for births overall (or the aggregate fertility rate). That suggests, as they conclude, that the baby bonus affected the tempo of fertility, but not fertility overall. In other words, women brought forward the birth of a second child as a result of the baby bonus, but did not have more children overall.

Of course, identifying structural breaks is not the same as estimating a causal policy effect, but the timing of the breaks provides suggestive evidence about whether policy changes may have mattered. However, one aspect of this paper in particular is kind of unusual. When Sinclair et al. outline the 2005 and 2015 structural breaks in second births, their results are shown in Figure 5 in the paper:

The grey line tracks the second birth rate each month, while the red line shows the overall trend. Notice that in the top panel of the figure, there is a clear change in the trend in 2005. The pre-2005 trend is downwards, and then there is a big jump upwards in the second birth rate in 2005, before it returns to its previous downward trend. The oddity occurs in the lower panel of the figure, where Sinclair et al. show a somewhat less downward sloping trend in the second birth rate up to 2015, before there is a big jump up, and then a much steeper decline. The second figure ignores that there was already a structural break in 2005, where the trend jumped upwards. It seems to suggest that the end of the baby bonus induced a big increase in second birth rate. Now, that could be true, if couples anticipated the removal of the baby bonus, and tried to have a baby before the bonus was removed. However, Sinclair et al. don't really discuss this.

A more interesting interpretation occurs if you squint at the top panel of Figure 5, and imagine one structural break in 2005, and then a second at 2015. In between those two years, the trend in the second birth rate might be mildly upwards. Of course, we don't know this for sure, as Sinclair et al. didn't test for multiple breaks in their time series. But perhaps their results overstate the case against the fertility impacts of the baby bonus. To be clear, these would still be impacts on the tempo of fertility, not on total fertility, but perhaps the baby bonus did have an enduring effect on bringing forward second births. This would be something for future researchers to follow up on.

Nevertheless, this research adds to the evidence that relatively generous cash payments like Australia's baby bonus are unlikely, on their own, to reverse declining total fertility rates. It is becoming abundantly clear that low fertility will be an enduring feature of future population change.

Read more:

• The pandemic 'baby bust' in other countries
• Cohort effects and the downturn in US fertility since the Great Recession
• The new economics of fertility
• The economics of the falling total fertility rate in New Zealand
• The economics of fertility in high-income countries
• Can fertility return to replacement levels?
• You can make future population decline disappear just by changing the way you categorise people and fertility

This week in research #126

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

• Klebl, Jetten, and Kirkland (open access) find that citizens living in nations with greater (vs. lower) levels of economic inequality were more likely to attribute responsibility for climate change mitigation to individual people rather than to governments (and businesses)
• Andersen, Grimsrud, and Lindhjem find that residential property prices in Norway decline in proximity to operating wind farms, with prices lower by 4 to 14 percent for properties within 2 km of a wind farm, and with effects declining to zero by 7 km from a windfarm

Book revew: Abundance

In the corners of the Internet that I inhabit, one of the most talked about books over the past year was Abundance, by Ezra Klein and Derek Thompson. And now, I have finally finished reading it. The key theme of the book is, unsurprisingly, in its title. Klein and Thompson reject a world of scarcity, arguing that we should instead embrace a world of abundance. What does that mean? In their view, abundance is about "having enough of what we need to create lives better than what we have had", and the process of obtaining abundance is about building and inventing an abundant future.

The book is understandably US-centric, and most of the book is devoted to outlining the various ways that the US has lost its way. The consequences are seen in the housing crisis, climate change and energy, declining research productivity, and many other challenges that are not just apparent in the US, but appear as a constant theme across Western developed countries (as well as many other countries).

Klein and Thompson focus on the key bottlenecks that inhibit the progress towards abundance. One of the bottlenecks they highlight is group conflict and the collective action problem. In that, they highlight the work of the economist Mancur Olson, specifically his book The Rise and Decline of Nations. In that book, Olson wrote:

    ...if organizations and collusions for collective action usually emerge only in favorable circumstances and develop strength over time, a stable society will see more organization for collective action as time passes.

While collective action could be used to drive the economy towards abundance, so often it is used instead to prevent it. To me, this sounded very much like the problem in the theory of public choice around the optimal number of people to be involved in a decision (for example, see this post, where I describe the trade-off between external costs and decision-making costs). The greater the number of people who must agree with a decision, the greater are the decision-making costs. And collective action, which by definition increases the number of people involved in decision-making, can increase costs and paralyse the decision-making process.

The book is written in an easy-to-read style. Those who are familiar with Klein's columns in the New York Times or Thompson's articles in The Atlantic will recognise this. There are also some funny moments, such as one bit on whether it is government inefficiency that means that it is expensive to dig tunnels in the US. Klein and Thompson list other countries with lower costs of tunnelling, then say:

We looked into it, and it turns out that all those countries also have governments.

Indeed, they do. Government doesn’t inherently make infrastructure construction more costly. The problem lies instead in particular ways that government works (or doesn't).

The most disappointing aspect of the book is that Klein and Thompson spend over 200 pages outlining the problems and making the case for abundance, but then they are short on solutions. They wave away that criticism by noting that:

It is easy to unfurl a policy wish list. But what is ultimately at stake here are our values.

That may be so, but perhaps one concrete solution as a starting point would have been good. Maybe a specific example where permitting reform has been successful in raising housing supply and alleviating high housing costs, or where changes in research and development funding have meaningfully improved innovation. Despite the lack of specific solutions, they do highlight the existence of trade-offs, which is refreshing given that other books have unrealistically utopic views of what can be achieved in the face of big challenges. However, there are some clear blind spots, one of which is a failure to engage meaningfully with the arguments for de-growth. While I don't place a lot of stock in de-growth arguments, that doesn't mean that they don't garner a lot of attention. Even if de-growth is ultimately unpersuasive, a book making the normative case for abundance should probably explain why abundance is preferable to sufficiency, restraint, or reduced consumption.

This book has been a bit of a rallying cry for sections of the left since it was published. It is definitely worth reading in order to better understand the problems and their underlying causes. It is also helpful to think about how we might do better in the future. It is just unfortunate that Klein and Thompson leave us hanging, and wondering what to do next.

Try this: The Mother of Econ

US high school student Saras Tolley has created a collection of AI-powered economics tools called The Mother of Econ. The tools include a 'shock simulator', which takes any news headline and determines what the supply and demand effects are, an 'econ paper decoder', which takes any NBER working paper, or journal article in the Journal of Economic Perspectives or American Economic Review, and creates a plain English summary, and ten other tools.

I put the 'shock simulator' through its paces. After all, if an AI can evaluate the economic implications of a news article using supply and demand, what will I do on this blog? Just kidding, there is lots that I could do. But nevertheless, the results were uneven. Faced with the news headline that I blogged about here, the shock simulator correctly drew a demand increase. However, when presented with the news headline that I blogged about here, it drew a supply decrease rather than a demand increase. So, I guess there is still a little more work required before my supply-and-demand blogging is replaced by AI!

Some of the other tools are really good to play with, including the 'shadow Fed', which applies the Taylor rule to determine what the US fed should do with interest rates and tracks that against actual Fed behaviour. There's also a 'US econ dashboard', which tracks economic statistics by state, and allows you to drill down to the county level (but only for some states, and only for some counties within states that have data - I was disappointed to find data on only six counties in New York, for instance).

Nevertheless, this is an interesting collection of tools, all powered by Gemini, and available for you to use at no (monetary) cost. Enjoy!

[HT: Marginal Revolution]

Koi Tū's case for a New Zealand population strategy

At the end of last month, Koi Tū Centre for Informed Futures released a report arguing that New Zealand needs a population strategy. The report, by Georgia Lala, Paul Spoonley and Sir Peter Gluckman, received a lot of media attention (see here and here and here), and attracted a response on The Conversation from my colleagues at Te Ngira Institute for Population Research. My view is that the case for a population strategy is strong, not because it would allow New Zealand to control demographic change, but because it would force governments to plan more coherently for changes that are already underway.

Now, Lala et al.'s argument is that New Zealand is facing a demographic inflection point. I’m not convinced that 'inflection point' is quite the right term, at least in the mathematical sense, but the underlying argument is sound. It is clear that New Zealand is facing a number of intersecting challenges, including:

1. Slowing population growth - Lala et al. note that "annual growth dropped to 0.7% between June 2024 and June 2025 from 1.7% between June 2023 and June 2024", and that slowing growth means that New Zealand faces a 'double-edged sword' of a constrained tax base and increasing costs;
2. Declining fertility - This is a long-run trend that all countries are facing, and no country has developed a sustainable policy solution (see here for more on that point);
3. Growing reliance on immigration - Immigration has become increasingly important for growing the labour force and population (but isn't a solution for population ageing), but the problem is that immigration (and net international migration) is very volatile, and New Zealand doesn't stack up well against other countries in the competition for global talent;
4. An ageing population - Population ageing has implications in terms of a smaller tax base, and higher healthcare and superannuation costs; and
5. Growing ethnic and cultural diversity - Lala et al. especially draw attention to diversity in Auckland, but it is a reality that is playing out across the country (it is just that Auckland is ahead of other places in terms of diversity).

The issues are just as important, if not more important, at the regional and local levels, and Lala et al. draw attention to that as well. [*] In the section on immigration, I felt like there was too much focus on citizenship (of emigrants), which misses the point that people leaving New Zealand are also increasingly diverse, reflecting the diversity of the New Zealand population. And the large net outflow of Māori in recent years, which is obvious from Figure 10 in the report, probably needed further comment. That matters because Māori migration patterns are not just another component of aggregate population change. They have implications for whānau, iwi, regional labour markets, and the government's obligations under Te Tiriti o Waitangi.

Lala et al. argue for a population strategy, which they define as:

...both actions by a government to identify demographic trends and, subsequently, actions to address the effects of such change.

This seems like a sensible recommendation, and you might be tempted to wonder why we don't have this already, given that national and local government should be keenly concerned about, and adequately planning for, population change. However, once Lala et al. spell out what would be required for a coherent population strategy, it becomes clear that New Zealand falls well short (and, indeed, they can't point to a single country that does all of the things they want from a population strategy for New Zealand). According to Lala et al., the enabling environment for a population strategy requires three things (emphasis is theirs):

First, a population strategy would help elevate key demographic topics above day-to-day political contestation...

Second, a population strategy would help elevate policy planning beyond an election cycle...

Finally, a coherent population strategy could enable strategic decisions across multiple sectors of government including between central and local government.

The problem is clear. We don't really have any of those elements. Decision-making related to population is highly politicised (think about immigration policy, or support for families, for example), policies are subject to reversal with every change of government, and there is little coherence of planning between central and local government (consider the example of Auckland housing, where central and local government are in constant disagreement).

Finally, Lala et al. argue for an independent population commission to:

...provide a robust governance and implementation model to ensure the effective execution of a population strategy.

There is a lot to like in this proposal for a population strategy. New Zealand definitely needs to be more intentional in population planning. However, there are also some serious challenges, as my colleagues Tahu Kukutai, John Bryant, and Polly Atatoa-Carr note in their article in The Conversation. They draw attention to fertility trends, which have proven stubbornly resistant to policy-induced change. They also point to migration, which is not as easy to control as many politicians believe. This is because New Zealanders have the right to live and work in Australia and vice versa, with large flows between the two countries. There is also a huge diaspora of New Zealanders living overseas, who have the right to return at any time (as we saw during the COVID pandemic). Kukutai et al. also argue that a population strategy should have diversity as a foundational design principle, rather than an afterthought. Finally, they note the challenges of adopting a data-informed policy-making, when the quality of population data is in question with the changes to the census.

Kukutai et al. raise some valid points. However, those are challenges to a strategy that should be confronted during its design and development, not a reason to avoid having a strategy at all. I've argued in public forums in the past that a population strategy may fit into the 'too hard basket', in particular because New Zealand can't manage the international migration flows of New Zealand citizens, and so much population change in New Zealand is driven by the economic cycle in Australia. I now believe that also isn't a good reason for not having a population strategy for New Zealand, it is a good reason for us to have a strategy so that these challenges can be met head-on.

The future may be uncertain, but the future demographic challenges that New Zealand will face are already visible. A population strategy would help New Zealand to grapple with those challenges in a more coherent way. At a minimum, such a strategy would need to connect migration settings, regional planning, infrastructure, housing, health workforce planning, Māori and Pacific population futures, and the future of population data. Good on Koi Tū for making this case, and hopefully the government is listening.

*****

[*] And Paul Spoonley (one of the report's authors) and I will be working together on some further research looking in greater detail at regional population change, in the near future.

David Oks on the bad business economics of airlines

Airlines are a strange business. They seem to have huge numbers of passengers, and yet we routinely hear about airlines struggling financially, entering administration, or shutting down. For example, in 2024 in Australia, Bonza collapsed, while Rex withdrew from major intercity routes after entering vountary administration (see this post for more on those examples). The most recent example is Spirit Airlines in the US, which shut down this month, after its second bankruptcy process in less than two years.

I just discovered David Oks's Substack, which has overnight become one of my favourites. The post that first attracted me was this one titled 'Why airlines are always going bankrupt', inspired by the Spirit Airlines story. Another recent post titled 'Why ATMs didn’t kill bank teller jobs, but the iPhone did' is also excellent, as is 'How funerals keep Africa poor'.

Oks's airline post has a huge amount of depth, so is difficult to summarise without losing part of the story. However, I'm going to try (but I really recommend that you read his entire post, as it is truly excellent).

Oks first notes that airlines are not just badly managed, they are structurally vulnerable and often fail to earn a positive return on capital. He then turns to the game theory of airlines, noting that there is an 'empty core' problem, meaning that there isn't any subset of the airline industry that can form a stable coalition, because some part of the coalition would always be able to make themselves better off by breaking away from the coalition. In other words, no group of airlines and routes is stable for long, because whenever capacity is tight and fares are high, another airline has an incentive to add seats. However, once those seats are added, the market can quickly become unprofitable.

The 'empty core' arises because airlines have high fixed costs, low marginal costs, volatile demand, weak product differentiation, and large minimum efficient scale. All of that means that adding one extra airline on a route, or one extra aircraft, can swing a market from being undersupplied and profitable to being undersupplied and unprofitable. So, what happens in the airline industry is that when there are few airlines, they are profitable and happy, but that encourages new airlines to enter, after which profits decrease until one or more of the airlines shuts down. And then the cycle starts over.

As you can see, there is a lot to unpack from Oks's post. Again, I encourage you to read the whole post. But the key points are, first, that airlines are always going bankrupt because the nature of the airline industry lends itself to a boom-bust cycle when airlines compete with each other.

Second, from the perspective of airline consumers (and, probably, governments as well) there is an uncomfortable trade-off. We want low fares from competition between airlines, and we also want airlines to be financially stable, but it may be difficult to have both. The pursuit of low fares can set off the cycle described above, with competition pushing fares down, leading to falling profitability, and eventually one or more airlines exiting the market. Airline financial stability, on the other hand, probably requires some limit on competition between airlines. One way of achieving this is to regulate airlines more closely, closer to the equilibrium that existed before deregulation in the late 1970s and early 1980s, when regulators had much greater control over fares, routes, and market entry. Airlines segmented the market, flew fewer routes, and were profitable in part because they were not actively competing with each other. The other alternative is to recognise that airlines can diversify into other markets. Oks’s most striking example of this is Delta:

...the most profitable airline in the United States, which started a fruitful partnership with American Express in 1996 and launched a co-branded card with them in 2008. Annual spending on Delta-branded American Express cards comes out to about 1 percent of U.S. GDP. In 2025, this produced about $8 billion in revenue for Delta, accounting for more than the entirety of its profit. That means that without the American Express partnership, Delta would be operating at a substantial loss. In effect, Delta’s aviation business is a loss leader for a much more profitable credit card partnership. So to the extent that Delta is now a good business, it is because it escaped the basic instability of the airline industry by becoming less of an airline.

Allowing airlines to operate in a cartel-like equilibrium, as airlines effectively did prior to the 1980s, doesn't strike me as a very positive solution, especially from the perspective of consumers. Having airlines shut down at short notice, cancelling thousands of flights, is clearly not good for consumers either. Perhaps then we need to tolerate airlines that try to sell us financial services, or unbundling the airline package and separately selling seat selection, checked baggage, meals, and so on (see this post about Jetstar's business practices, or this one).

The economics of castles

When I'm in Britain or Ireland, one of my favourite sightseeing trips is to visit medieval castles. Even the ruined ones are fun to visit. Actually, maybe the ruined ones are more fun to visit, because you get to imagine what they would have looked like in their heyday. Britain and Ireland are full of castles, many of which were built by and housed local nobles. In fact, in relative terms there were very few royal castles, which the literature in history and economic history has interpreted as a sign that centralised states were weak.

However, this recent article by Desiree Desierto and Mark Koyama (both George Mason University), published in the journal European Economic Review (ungated earlier version here) challenges that view. They instead show that there was an economic logic to the proliferation of private castles.

Desierto and Koyama develop a game theoretic model of medieval states, which first recognises that the monarch cannot rule alone but must rely on a coalition of local lords or barons. Each lord agrees to join the coalition, and pledges resources to the monarch in exchange for a (however small) share of control of the kingdom. The monarch can renege on the agreement, taking the resources without offering a share to the lord. However, the lord would then rebel, leaving the coalition. What allows the lord to leave the coalition, and gives them bargaining power, is the presence of their own castles, since they can retreat to their castle when they rebel against the monarch. Without a private castle, the lord would have little bargaining power, and would anticipate the monarch reneging on any agreement, and so they would not join the coalition in the first place. In economic terms, the castle gives the lord an outside option. 

So, the monarch tolerates private castles held by the lords, because the presence of those castles gives the lords the feeling of security they need to join the kingdom. And, in turn, the presence of those castles disciplines the monarch. Rebellions are more costly to suppress when the lords can retreat to a well-defended private castle. The lords' outside option increases the feasibility of rebellion and ensures that the monarch mostly keeps to their agreement with the lords.

In short, the private castles induce an equilibrium where the kingdom is larger and more stable than it would be without them. Notice that this is the opposite of the conventional view that private castles represent a sign that a state was weak.

Desierto and Koyama support their argument with descriptive evidence, noting that:

In Norman England after the Conquest, castles were built across the country: by 1154 there were 225 baronial castles (compared to 49 royal castles) in England... Baronial castles allowed the Dukes of Normandy to extend their authority over the far larger territory of Anglo-Saxon England. Similarly, in the twelfth century Angevin rule expanded over much of France as semi-independent lords in Gascony accepted the lordship of Henry II.

Desierto and Koyama also note that powerful medieval monarchs did not act to systematically eliminate private castles, and in fact the monarchs often gave away their own castles to local lords. And the power of the lords did keep the monarchs in check - a lord's probability of rebelling against the monarch was positively correlated with the number of private castles in the lord's family network. That last point might sound contradictory, but since castles make rebellion by lords a more credible threat, this can deter monarchs from reneging and reduce the number of rebellions overall. However, when rebellion does occur, lords connected to more castles were more likely to participate in the rebellion.

So, if private castles were so important for the stability of medieval states, why did private castles eventually disappear? Desierto and Koyama note that:

The answer is military technology, not the rising power of the state. Technological changes and the associated ‘‘military revolution’’ that took place beginning in the late Middle Ages reduced the value of medieval fortifications. The main technological innovation was the introduction and improvement of gunpowder weapons, which began in the fourteenth century but only really began to have a serious impact in the fifteenth century with the introduction of iron cannonballs.

This is not a new insight, but it does align well with their model. However, it somewhat reverses the logic of the conventional view, which is that greater state power, along with military technology, reduced the prevalence of private castles. Instead, in Desierto and Koyama's model, the rise of gunpowder reduces the ability of lords to retreat to a well-defended castle in the event of a rebellion (because the castle could not be as well-defended against cannons). This reduced the lords' bargaining power, giving the monarch and the centralised state greater power. As a result, the state should become less stable. In support of this, Desierto and Koyama use the Wars of the Roses in England as an example:

England experienced a large number of rebellions and civil wars between 1450 and 1500. These conflicts are conventionally grouped under the label of the Wars of the Roses (1455–1485), but the period of weak state capacity and frequent rebellion extended from Jack Cade’s uprising in 1450 through Perkin Warbeck’s invasion and the Second Cornish Uprising in 1497. The causes of these rebellions were complex, multifaceted, and varied across cases. Nonetheless, the frequency of civil war during this period is consistent with our model’s prediction that a decline in the military value of castles would destabilize feudal realms.

And so, as gunpowder reduced the military value of castles, private castles became much less useful as a source of bargaining power for lords. That helps explain why the medieval pattern of widespread private castles gave way to state-controlled castles from the mid-15th Century onwards. Now, I'll be thinking more carefully about the vintage of the castles I visit on my next trip to Europe next month!

This week in research #125

Here's what caught my eye in research over the past week (which was clearly another quiet week):

• Sinclair et al. (open access) compile a dataset of monthly birth rates by maternal age and parity for the Australian state of Victoria over the period from 1983 to 2020, and apply a variety of different time series models to the data, finding that Australian family policy has mainly altered the timing of births rather than reversing the long-run fertility decline, and that the Australian 'Baby Bonus' led to only a short-lived impact, concentrated among second births

The Hamilton vs. Wellington population showdown

Some of my research was profiled on the front page of the Waikato Times today (paywalled):

Forget Wellington — Hamilton is on track to overtake the capital within 14 years.

New University of Waikato projections show the city’s population could climb to 242,716 by 2040, cementing its status as New Zealand’s fastest-growing city.

Hamilton’s population projection is under the “high variant” forecasts — the growth estimates council staff are recommending, and which the Government requires councils to use when planning their Long Term Plans.

If that is compared to Stats NZ's and the Wellington Regional Growth Framework estimates for Wellington for the same year, Hamilton's population will be larger by 2716 people.

Now, this Hamilton versus Wellington head-to-head population battle seems to be attractive to the media (see this post from 2019, talking about this 2019 Waikato Times article). However, they've got things wrong this time, for a couple of reasons.

First, they are comparing Hamilton City with Wellington City, which is a valid comparison of city council areas, but may not be the comparison many people have in mind. I'll come back to that point at the end of the post.

Second, and more importantly, you shouldn't compare a projection from one source, based on one set of assumptions, with a projection from a totally different source, based on a different set of assumptions. Especially when projections from the same source are available, using consistent assumptions. Otherwise, you are not comparing apples with apples.

So, let's make some consistent comparisons. Stats NZ's projections are available on Aotearoa Data Explorer, Stats NZ’s online data tool. Search for "subnational population projections", and then scroll down to "Subnational population projections, by age and sex, 2023(base)-2053". Stats NZ offers three variants (low, medium, and high) of 2023-base population projections. The difference between the variants is that low variant projections assume low fertility, high mortality, and low international migration, while high variant projections assume high fertility, low mortality, and high international migration (and the medium variant projection is, obviously, in-between the low and high). Here are the three variant Stats NZ projections for Wellington City and Hamilton City:

The bold lines are for Hamilton City. The dotted lines are for Wellington City. The low, medium, and high variants are coloured blue, green, and brown respectively. The key thing to notice is that the lines cross over. Where the lines of the same colour cross, that is the point in time when Hamilton catches up with Wellington under that projection variant. So, with Stats NZ's projections, Hamilton is projected to be larger than Wellington by 2038 under all three projections. If we do a linear interpolation (because Stats NZ only reports their projections for five-year intervals), then Hamilton is projected to be larger than Wellington by 2034 in the low and medium variant projections, and by 2035 in the high variant projections.

Turning to the University of Waikato (UoW) projections (which I produced), there are also three variants (low, medium, and high) that can be interpreted similarly to Stats NZ's projections. The methods and assumptions differ from those used by Stats NZ. These are the projections that Hamilton City Council uses in its planning (as do several other local councils). Here are the three variant UoW projections for Wellington City and Hamilton City:

In my projections, Hamilton is projected to be larger than Wellington by 2040 in the low variant projection, by 2048 in the medium variant projection, and by 2066 in the high variant projection (which is beyond the projection horizon for Stats NZ projections as they only project for 30 years).

Why the difference? The difference between the timing using Stats NZ projections and the timing using my projections is due to differences in assumptions and the underlying models. It would take a long post to unpack all the differences in detail. The differences between the low, medium, and high variants are easier to explain. Wellington has a head start - it was much larger in 2023 than Hamilton. However, Hamilton has both higher fertility and greater net migration than Wellington. That head start makes a bigger difference in the high variant projections than in the low variant projections, because the higher fertility and international migration in the high variant projections allow Wellington to maintain that lead for longer. In the low variant projections, Hamilton's higher fertility and net migration allow it to catch up much faster. In other words, because Wellington starts from a larger population base, assumptions that lift population growth across the whole country add more people to Wellington in absolute terms, delaying Hamilton's catchup, even though Hamilton’s underlying growth rate is higher.

What is interesting is that the differential effect between low-variant and high-variant projections doesn't seem to be anywhere near as prominent in the Stats NZ projections as it is in my (UoW) projections. In part that is because the uncertainty expressed in my projections (proxied by the difference between the low and high variant projections) is much higher than the projections by Stats NZ. I'm comfortable with that, given that international migration in particular is highly uncertain. So, we should expect a fairly high degree of uncertainty when we project future population.

One final thing to note is a point I made in my 2019 post on this topic. Wellington City is only one part of a larger urban area ('Greater Wellington') that also includes Porirua City, Upper Hutt City, and Lower Hutt City. There is no projection that has the Hamilton urban zone catching up in population to the broader Wellington urban zone any time soon. I suspect that many people would be flabbergasted by the suggestion that Hamilton might become larger than Wellington. Many of those people would be thinking about Greater Wellington, and they would be right.

So, Hamilton will eventually be New Zealand's number three city council area in terms of population. However, the celebrations could easily be put on hold by a council amalgamation process that the government has started, which could conceivably merge some Wellington councils together, putting their combined population out of reach of Hamilton for the foreseeable future.

[HT: The incomparable Emeritus Professor Jacques Poot]

Read more:

• Hamilton won't be our second largest city any time soon

Kansas City rent strike rebalances relative bargaining power towards tenants

Today in my ECONS101 class, I covered search models of the labour market. In these models, a matching between a worker and an employer creates a surplus, which is then shared between the worker and employer depending on their relative bargaining power. The greater the worker's relative bargaining power, the greater the share of the surplus the worker will claim, meaning that wages will be higher. The lesser the worker's relative bargaining power, the lesser the share of the surplus the worker will receive, meaning that wages will be lower.

Search models are not just useful for thinking about labour markets though. They can be used in any situation where two (or more) parties are matched together in a way that creates a surplus, which is then shared between them. A joint venture between firms is an example. So is a marriage. In both cases the parties match together, create a surplus, and then share that surplus based on their relative bargaining power. A further example exists in the market for rental housing. Landlords and tenants are matched together. That matching creates a surplus, which is shared between them. And relative bargaining power matters, as this Yahoo!News article from the end of last year (originally published in the Washington Post) demonstrates:

In the two years she has lived at Bowen Tower, Cynthia Barlow’s apartment has flooded, been plagued by mold and been infested with cockroaches. The building’s heat stopped working. When the elevators broke over the summer, emergency workers carried a sick neighbor down 10 flights of stairs.

Meanwhile, Barlow’s rent for the two-bedroom unit increased from $993 per month to $1,213.

Growing frustrated, she hung fliers in the elevators and hosted potlucks, persuading a majority of tenants in the 90-unit building to join the Bowen Tower Tenant Union and stop paying rent until conditions improved. So far, they’ve won a meeting with the landlord, and a judge has knocked thousands of dollars off the rent debt of one resident facing eviction.

“I got tired of being treated the way I was treated,” Barlow said.

The rent strike is part of a strategy that housing activists have started to replicate in midsize cities across the country.

When tenants organise themselves into a tenant union, then ceteris paribus (holding all else equal) that increases the tenants' relative bargaining power with the landlords. If the tenants were to all leave their apartments, then the landlord has to search for new tenants, which is costly. Now, an individual tenant could threaten to move out, but filling one apartment with a new tenant is relatively easy. When an entire block of tenants makes the same threat, the landlord is facing serious disruption. More importantly, a rent strike means that, instead of losing or negotiating with one tenant at a time, the landlord faces coordinated action including withholding rent, legal disputes, repair demands, and public pressure from many tenants at once.

The tenants' increased bargaining power (due to unionising) should result in lower rents and improved maintenance of the apartments. The way this worked in practice was that tenants, feeling more powerful with the backing of other tenants, stopped paying rent. However:

Barlow is scheduled for eviction court in January, and Bowen Tower management hasn’t renewed her lease.

There is only so far that tenants can push their greater relative bargaining power, particularly where alternative affordable housing is scarce and legal protections are weak. However, the Bowen Tower case also shows why collective action may matter. Ultimately, the tenants appear to have prevailed and won substantial concessions. This later article notes that the rent strike ended after four months, when the landlord promised repairs and lower rents, and after tenants had withheld nearly US$110,000 in rent. 

Tenant unionisation does not guarantee success, and the risks to tenants can be substantial. But it does mean that tenant unions can shift the bargaining outcome, and the share of the surplus, especially when they are able to sustain coordination long enough to make the landlord’s alternative more costly than negotiation.

[HT: New Zealand Herald]