New evidence that declining fertility rates may not stabilise

It is not news that total fertility rates across most countries are declining, and have been for some time (see the links at the end of this post). However, in spite of declining fertility, many population projections assume that fertility decline will come to an end, or even reverse. For example, the UN World Population Prospects projections assume that fertility rates eventually enter a relatively stable phase, with an average long-run level of about 1.6 births per woman, although the assumed stabilisation level varies across countries (see page 29 of this methodological report). Similarly, Stats NZ assumes that the median period total fertility rate will slowly decline from 1.59 in 2025 to 1.55 in 2054 and beyond (see here).

Given that it has been relatively rare for total fertility rates to reverse trajectory, how realistic is it that they will attain some stable equilibrium in the future? This new NBER Working Paper by Diane Coffey, Michael Geruso, Dean Spears (all University of Texas at Austin), and Sangita Vyas (City University of New York), digs into the data to provide an answer. Specifically, they look at data on:

...period total fertility rates (TFRs) for all country-years since 1950 in the United Nations World Population Prospects, in completed cohort fertility for country-cohorts in the Human Fertility Database, and in fertility measures from subnational geographies in India, Japan, and the United States.

It is worth taking a moment to explain the difference between a period total fertility rate and a cohort total fertility rate. Most of the time, the statistics we see reported are period total fertility rates. Those are the number of births a woman would have across their lifetime if they experienced the age-specific fertility rate at each age in that year. Of course, each woman only experiences the age-specific fertility rate of one age in any one year, not all of them collectively. That brings us to the cohort total fertility rate, which is the average number of births each age-cohort of women (born in the same year) actually have across their lifetime. Completed cohort fertility is computed after an age cohort of women has effectively moved out of childbearing age, which is why this statistic isn't reported often - you have to wait until women are over 50 years old before you can be confident that you have an accurate measure of the cohort total fertility rate. This distinction between the two measures matters because period fertility rates can decline if women delay having children, even though the cohort fertility rate (the average number of children women have over their childbearing years) remains unchanged.

Now, coming back to the working paper by Coffey et al., they use phase diagrams to illustrate the trajectory of total fertility rates over time. A phase diagram graphs the change in a variable against the level of the variable. It essentially shows, for each level of the variable, whether the variable is increasing, or decreasing. If a total fertility rate is to stabilise at some level, then the phase diagram should show that for low levels of the rate, the change is positive, while for high levels of the rate, the change is negative. The level where the change is equal to zero is the level at which the rate would stabilise. In other words, for fertility rates to stabilise at 1.6 births per woman, we would expect them to increase if the rate is below 1.6 and decrease if the rate is above 1.6.

What do Coffey et al. find? The results for past data from the UN World Population Prospects is summarised in Figure 3 from the paper:

Notice that for most levels of the total fertility rate, the point estimate of the change (growth) in the rate is negative. The most charitable interpretation of these results is that maybe there is some weak evidence for a cross-over at a total fertility rate of between the 1.25-1.35 bin and the 1.35-1.45 bin. However, none of the estimates below the 1.45-1.55 bin are statistically significantly different from zero, and the point estimates at the lowest total fertility rates are negative.

Moving onto data from the Human Fertility Database, the results are summarised in Figure 4 from the paper:

In this case, there is no evidence to support a stabilisation of cohort total fertility rates in these data. All point estimates are negative, and all of those from the 1.45-1.55 bin and above, are statistically significantly below zero. The results for Japanese prefectures since 2010, US states since 2010, and Indian districts (based on data from the Demographic and Health Survey 2019-2021) all similarly show negative and statistically significant change in total fertility rates across almost all levels of observed total fertility rates (the one exception is the 1.05-1.15 bin for Japanese prefectures, but that result is positive but statistically insignificant).

Taken altogether, these results demonstrate that there is no evidence that declining fertility rates will reverse course, at least not on average across countries, or across subnational areas in the three countries Coffey et al. looked at. There isn't even any evidence that total fertility rates will stabilise, at least not at the levels that have been observed so far.

Low and declining total fertility rates are the norm across developed countries, and increasingly in developing countries as well. They aren't returning to replacement levels. That doesn't mean that fertility rates will decline indefinitely, but it does mean that assuming they will stabilise seems unlikely to be borne out. So, policymakers might be better off planning for continuing low fertility, rather than blaming pet ownership and waiting for a rebound.

[HT: Marginal Revolution]

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?

Pets and children may be complements, not substitutes

When you see people treating their fur-babies like children, you might be tempted to wonder to what extent people substitute pet ownership for raising children. You wouldn't be alone in considering the relationship between pet ownership and raising children. In 2022, Pope Francis decried the low global birthrate, noting that "dogs and cats take the place of children". But are dogs and cats really taking the place of children?

A new working paper by Kuan-Ming Chen, Ming-Jen Lin (both National Taiwan University), Hau-Hung Yang (Academia Sinica), and Shirley Yu-Hsuan Yen (National Taipei University) provides an answer, using data from Taiwan. They take advantage of dog and cat registry data from 2009 to 2020, matched to tax administration records that identify families and household composition.

To work out whether two goods (in this case, children and pets) are substitutes or complements, we need to look at the cross-price elasticity of demand, which relates the percentage change in quantity demanded of one good to a change in the price of another good. When the cross-price elasticity is a positive number, the two goods are substitutes. When the cross-price elasticity is a negative number, the two goods are complements.

Now, there are two interesting things about this paper that would be familiar to intermediate microeconomics students. First, Chen et al. are careful to distinguish between Marshallian cross-price elasticities (named after late 19th Century economist Alfred Marshall) and Hicksian (compensated) cross-price elasticities (named after 1972 Nobel Prize winner John Hicks). The difference between the two is that Marshallian cross-price elasticities combine both the income and substitution effects, while Hicksian cross-price elasticities look at only the substitution effect (see this post for more on the difference between an income effect and a substitution effect).

The second interesting thing is that Chen et al. effectively use a Slutsky decomposition to identify the cross-price elasticity of children to a change in the price of pets. This essentially involves two equations. The first equation is Slutsky symmetry:

The left-hand-side variables are the share of household spending on pets (spet) multiplied by the Marshallian cross-price elasticity of pets to a change in the 'price' of children. The first term on the right-hand side is the share of household spending on children (schild) multiplied by the Marshallian cross-price elasticity of children to a change in the 'price' of pets. The second term on the right-hand side multiplies the two shares of household spending by the difference in the income elasticity of demand for children and pets.

The second equation is much simpler. The Hicksian cross-price elasticity of children to a change in the price of pets is equal to the equivalent Marshallian cross-price elasticity plus the share of spending on pets multiplied by the income elasticity of demand for children. In other words, this equation adjusts the Marshallian elasticity to remove the income effect, leaving just the substitution effect.

So, the Hicksian cross-price elasticity of children to a change in the price of pets can be calculated using information about all of the other variables in those two equations. And gathering estimates of those other variables is what Chen et al. set about doing.

First, to estimate the cross-price elasticity of pets to a change in the 'price' of children, Chen et al. look at how increases in the 'child bonus' paid by local municipalities affect the registration of pets, since the child bonus affects the up-front cost (or 'price') of having children. Using an event study, they find that:

Both cats and dogs respond, and they respond in an ordered way. In the first post-treatment year, new dog acquisitions rise by 0.067 percentage points on a pre-treatment base of 0.332 percentage points, a 20.1% increase (𝑡 = 2.40); new cat acquisitions rise by 0.0136 percentage points on a base of 0.125 percentage points, a 10.9% increase (𝑡 = 2.36). Cats are therefore complements with children as well, but the proportional response is roughly half as large as for dogs.

...the implied Marshallian cross-price elasticity for cats is approximately −0.68, against −1.24 for dogs.

Remember that the Marshallian cross-price elasticity doesn't remove the income effect of the price change. To calculate the Hicksian cross-price elasticities, Chen et al. therefore need to estimate the other parts of the equation. So, next they estimate the income elasticities of demand for children and pets, making use of an interesting lottery:

Taiwan’s sales tax system provides a natural experiment for income shocks through its uniform invoice lottery program. As part of a nationwide effort to reduce tax evasion, nearly all consumption receipts are eligible for bi-monthly lottery drawings, with prizes reaching up to NTD10 million. Receipt holders are randomly selected, and prizes are distributed regardless of income level or background. These awards thus constitute plausibly exogenous, positive income shocks.

This lottery had already been used by other researchers in an event study (where the treatment is receiving a lottery windfall of at least NTD2 million) to estimate the income elasticity of children, finding it to be 0.15, meaning that children are a normal good. Following the same approach, but looking at the income elasticity of pets, Chen et al. find that the income elasticity of dogs is 0.11. Annoyingly, they don't report the income elasticity of cats (although they use it in their calculations later), although their other estimates suggest that it may be close to zero.

Finally, Chen et al. estimate the share of expenditure on children and pets. Using data from the Family Income and Expenditure Survey, they find the share of household spending on pets is 0.032 (about 3.2 percent). For children, they use an 'equivalence scale approach', which weights children in adult-equivalent terms at 0.3, and noting that would imply a share of spending in a three-person household of about 17 percent, they adopt a conservative share of household spending on children of 0.2.

Now, with all of the pieces together, Chen et al. move on to estimating the Hicksian cross-price elasticity, finding that it is equal to -0.198 for dogs, and -0.089 for cats. In other words, dogs and children are complements, and so are cats and children. The results are also reasonably robust to various alternative assumptions about the income elasticities and expenditure shares.

Chen et al. then go on to explore why pets and children might be complements. By looking at the timing of pet ownership and childbearing, they identify that it is much more common for families to have pets first, and children second. They infer from this that:

Pets may serve as a “starter family,” letting prospective parents rehearse caregiving and learn about their own taste for family life before a birth.

Of course, the mechanism results do not eliminate other explanations. The results are consistent with the 'starter family' channel, but it is also possible that people who are already planning to have children are more likely to register a pet beforehand. Also, these results are based on Taiwan. Perhaps pets and children are substitutes in other countries - we won't know unless further research is done. Nevertheless, these results are a good place to start, and should make us really question whether dogs and cats are taking the place of children.

[HT: Jacques Poot]

William Nordhaus, 1941-2026

I was saddened to hear of the passing of William Nordhaus last Friday. Nordhaus won the Nobel Prize in 2018 "for integrating climate change into long-run macroeconomic analysis", and it is his work on climate change and economics that he might be best known for. However, that was far from his only contribution to economics. My ECONS102 students might recognise Nordhaus as one of the first economists to formally analyse the tradeoff between strong intellectual property rights, which provide incentives for creation, but lead to under-consumption, and weak intellectual property rights, which can lead to optimal consumption, but lead to under-investment in idea creation (for more on that tradeoff, see this post).

My favourite Nordhaus contribution was actually some work he did on the change in the cost of light over time. The original research paper is available here, but the story is best told in this article from the BBC:

Back in the mid-1990s, an economist called William Nordhaus conducted a series of simple experiments with light.

First, he used a prehistoric technology: he lit a wood fire.

But Prof Nordhaus also had a piece of hi-tech equipment with him - a Minolta light meter.

He burned 20lb (9kg) of wood, kept track of how long it burned for and carefully recorded the dim, flickering firelight with his meter.

Next, he bought a Roman oil lamp, fitted it with a wick, and filled it with cold-pressed sesame oil.

He lit the lamp and watched the oil burn down, again using the light meter to measure its soft, even glow.

Bill Nordhaus's open wood fire had burned for just three hours on 9kg of wood.

But a mere eggcup of oil burned all day, and more brightly and controllably.

Why did he do this?

He wanted to understand the economic significance of the light bulb.

His research showed just how dramatic the fall in the cost of light was. Drawing on his work, the same BBC article notes that 60 hours of labour:

...had once produced the equivalent of 54 minutes of quality light [but] now produced 52 years.

That fact demonstrates just how powerful technological progress and economic growth have been over the very long run. But Nordhaus's deeper insight was that simply looking at prices and economic growth alone can lead us to miss the enormous improvements in living standards. The significance of that cannot be overstated.

I'm surprised that there doesn't appear to have been much coverage of Nordhaus's passing. Nevertheless, Yale News has a good obituary. And while searching for some others, I ran across this podcast from earlier last month, that neatly covers his life in the way a good obituary would.

Nordhaus's work extended far beyond the climate economics he is most known for. His research on the cost of light is a reminder that seemingly ordinary things can nevertheless tell important stories. He will be missed.

Is it better to publish more papers, or longer papers?

Academics get rewarded for their research output. More papers, published in higher-ranked journals, is a large part of the metric against which academics are judged. Notice that there are two aspects to that metric though - quantity (more papers) and quality (higher-ranked journals). Since it usually requires more effort to successfully publish a paper in a higher-ranked journal, is there a trade-off between quantity and quality of publications?

This 2014 article by my colleague John Gibson, published in the journal Economics Letters (sorry, I don't see an ungated version online), asks a related question about how we measure the quantity of research output - is it better to publish one long paper, or several papers that in total have the same number of pages? To answer that question, Gibson links the 2010 salaries of 223 economists from the University of California system to their publication histories (in terms of the number of publications, the quality of journals they were published in, and the number of publication pages), while controlling for seniority, experience, gender, campus, and whether the economist held a named chair or was a Nobel Prize winner.

Gibson notes that there is a wide range of journal ranking and weighting schemes, so he applies nine different ones. He finds that:

Six of the nine equations show a significant positive effect on salary from publishing more articles, conditional on the total number of quality-adjusted pages. The size of the effect is not trivial; on average, a standard deviation increase in the count of articles holding pages constant increases salary by 15 log points.

Ok, so publishing more articles is associated with a higher salary. And the difference is not small - 15 log points is equivalent to a salary increase of about 16 percent. What about more pages? On this, Gibson finds that:

...if an econometric ‘horse race’ is run between total pages and counts of articles, Vuong non-nested tests favor the model using total pages as being closer to the truth, even in the regressions where counts and pages are both statistically significant.

In other words, the total number of pages published seems to be a better predictor of academic salary than the total number of articles published, even though the number of articles also matters when holding the number of pages constant. How much does this difference matter? Gibson does some back-of-the-envelope calculations, finding that:

The predicted salary with a [Journal of the European Economic Association] article added to lifetime output is $195,980. Adding just as many pages to the literature with three Economics Letters articles increases predicted salary to $196,970—an annual gain over the single-article strategy of $990. This average economist is 16 years into their career, so with a 40-year career and five percent discount rate, the net present value of publishing three Economics Letters rather than one JEEA is $13,700.

Of course, these results are correlations rather than causal estimates, so we can't conclude that splitting a long paper into several shorter ones would necessarily increase an economist's salary. Nevertheless, the results imply that not only does quantity of research seem to matter, but how that quantity is broken up matters too. So, having lots of small articles published may lead to a higher salary for academic economists. That conclusion aside, one of the best aspects of this paper, you may have noticed, is that Gibson published it in... Economics Letters.

This week in research #150

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

• Antler, Bird, and Fershtman (with ungated earlier version here) develop a theoretical model of dating and marriage
• Bowden, Rodriguez, and Weingarten (with ungated earlier version here) find that academic leniency increases high school GPA, but significantly reduces effort among lower ability students, based on lower attendance and lower numerical grades

PhD students' growing use of generative AI in STEM subjects

I see a large number of initial research proposals, sent to me as part of a cold-emailing strategy by prospective PhD students. The vast majority of those proposals are now obviously written by generative AI. For the most part, that makes the proposal largely worthless in terms of judging the quality of potential PhD students. If anything, it is a negative signal about their ability to conduct primary research. If they can't write a short initial proposal without significant help from generative AI, how will they cope with writing research papers and a thesis?

No doubt I am being too harsh in my approach. However, when you receive three or four cold emails per day from prospective PhD students, as I do about this time of year, you have to be a bit ruthless. I know some colleagues that delete those emails unread. I at least want to give these prospective students a chance to capture my attention.

My own experience is based on a sample of one. What does the evidence tell us about the growing use of generative AI by PhD students more generally? We receive an answer to that question in the context of STEM PhD students in this new NBER Working Paper (sorry, I don't see an ungated version online) by Daniel Gross (Duke University), Dror Shvadron (University of Toronto), and Hansen Zhang (Duke University). They collected data on US-trained PhD students from 2019 to May 2026, ran their dissertation abstracts through the AI-detection tool Pangram, and then collected data from each student's LinkedIn profile to determine their post-graduation outcomes. The growth in generative AI writing in dissertation abstracts is striking, as shown in Figure 1 from the paper:

Prior to 2023, almost no dissertation abstracts are flagged as having AI content. By the first five months of 2026, 29.4 percent of abstracts contain AI writing (at least, according to Pangram). Looking across fields, AI adoption has been most rapid in civil engineering and computer science (both around 50 percent in 2026), and lowest in mathematics and statistics (17 percent in 2026).

Gross et al. then dig into the characteristics of the PhD students using generative AI in their dissertation abstracts, finding that:

Non-native English-speaking students use AI far more intensively than native speakers... Among 2026 graduates, 41% of dissertations by students educated in non-English-speaking countries have AI-generated abstracts, against 23% for students from English-speaking countries...

AI use also increases at institutions with lower research intensity and programs of lower rank. In the 2026 cohort, 28% of dissertations at R1 universities have AI writing, compared with 31% at R2 universities and 37% at other doctoral institutions, and this gradient is particularly strong among US-origin students (foreign-origin students write with AI at high rates at all institutions...).

Gross et al. then look at where students go after receiving their PhD, finding that:

Among US-origin graduates, those with AI-written dissertations are 7.4 p.p. less likely to be employed in academia in 2026 (17% over the 44% base rate) and 8.0 p.p. more likely to be employed in industry (16% over a 50% base), with no difference in government employment. These differences attenuate substantially for likely foreign-origin graduates. We also find differences within academia: conditional on academic employment, graduates with AI-written dissertations are 5.4 p.p. more likely to hold non-tenure-track positions (on a base of 12%), with no significant difference in the likelihood of tenure-track or postdoctoral positions.

Gross et al. explicitly caution against taking a causal interpretation of the effect of generative AI use on student outcomes. I didn't interpret the results that way, instead seeing them as perhaps indicating that students with dissertation abstracts written by generative AI may be lower-quality students, or those with less of an academic focus for their future employment. Alternatively, some students may simply be using AI to overcome language barriers, or to polish their writing, rather than to substitute for their own research skills. However, the results don't definitively establish either of those interpretations either. 

What the results do show is more banal - PhD students are increasingly using generative AI. Of course, it is important to keep in mind that these results tell us about the prevalence of AI-generated writing in dissertation abstracts, rather than the dissertation more generally. Since generative AI is quite good at summarising, and that is essentially the purpose of an abstract, I would expect AI-generated writing to be more prevalent in abstracts than in the main text of the dissertations.

Finally, while these results apply to STEM subjects, it seems likely that this is indicative of a more general trend across all fields. If this trend continues, it will not be long before AI-assisted dissertation writing becomes the norm rather than the exception. What we should be wondering now is whether that trend will hamper PhD students in their development as researchers, or not.

[HT: Marginal Revolution]

Read more:

• Lessons from Joshua Gans on AI for economics research
• This is not how generative AI should be used in research

Generative AI and the future of the university

Universities and other educational institutions have had a few years now to come to grips with generative AI and its implications. For universities, what I have seen is a lot of angst about student cheating and the impacts on learning, courses set up to take advantage of the demand for students with AI skills, but mostly a lot of tinkering around the edges and hoping for the best.

A new working paper by Jason Potts (Alfaisal University) outlines the fundamental challenge to the entire business model of universities. Potts notes that universities are a very complex multi-sided platform market, matching at least fifteen disparate groups including, but not limited to, students, employers, and government.

Potts also notes that what universities offer to students is not content at all, but a 'warrant' - a signal to employers of the student's quality (see my post here for more on that point). This overstates the case of course. Universities do offer students the opportunity to develop important knowledge and skills. Even Bryan Caplan, in his book The Case Against Education (which I reviewed here), acknowledges that education develops human capital, despite arguing that most of the financial return to education comes from signalling.

Now, the challenge is that generative AI makes content delivery a commodity, so Potts argues that universities should concentrate more on providing the warrant of quality, essentially becoming a certifying organisation that provides a signal of the quality of students to future employers. He notes that:

The strategic form this implies is a university that examines and certifies as its primary business, and that treats instruction as a service which may be produced internally, purchased, or left to the student. Almost no one working inside a university right now wants this. But it is the logical direction this new wave of technologies is pushing the university.

I think Potts is right that no one wants this outcome. But he is also right that the value that universities provide to students will be increasingly embedded within the quality of the signal. And it is there that generative AI presents another challenge. Effective signals need to be able to separate high-quality and low-quality students, and one of the ways that occurs is by education being more costly (more difficult or time-intensive, rather than necessarily more financially costly) for lower quality students. Generative AI lowers the cost of producing assessment work that is of high quality, meaning that low-quality students are more difficult to distinguish from high-quality students. The signal value of education is reduced, unless universities make changes to preserve the value of the signal. On that point, Potts argues to:

...move the assessment to a modality where the production cost remains type-dependent. At this stage, the best candidate is probably a return to the viva, common in earlier times as an observed, embodied, high-bandwidth, unscripted examination conducted as an oral defense, or as live problem-solving before an examiner... This is expensive per student and is complementary with cheap AI-delivered instruction.

I'm not convinced that oral examination is the only way to achieve this aim. Properly invigilated in-person written exams are similarly resistant to unauthorised AI use, and are considerably more efficient than oral examinations. However, they are not appropriate to all courses, and may best suit courses where basic foundational knowledge needs to be assessed (although noting that it is possible to assess higher-level skills in a written exam as well). Potts notes that, in terms of university revenue, this means:

...open entry, examination-only pathways to a degree, and pricing on the assessment rather than on tuition or contact hours.

So, in Potts' view, universities wouldn't necessarily do any teaching, only assessing. Or rather, they might do teaching, but it would be complementary to their main activity of assessing students and certifying their knowledge and skills. 

There is a positive element to this for some university academic staff though. If you think through the incentives here, it becomes clear that the endpoint of this transition could easily be for universities to become diploma mills, offering certifications to paying students that bear no meaningful relation to students' actual learning, knowledge, or skills. Universities can only avoid that outcome if they can credibly demonstrate to employers that the credentialling process is not influenced by student demands. And so, academic staff retain some academic freedom:

Tenure exists so that an examiner can fail a candidate, reject a result, or refuse a certification without personal consequence. It is therefore the certifying body that must inherit the collegiate form (i.e. peer judgement, protected tenure, academic freedom) while the instructional arm can be run on ordinary managerial lines.

I'm not convinced that the market will discipline universities to the extent that Potts believes. Diploma mills already coexist alongside credible institutions, demonstrating that there is demand for credentials of questionable value, although some employers may already recognise those differences. The more serious problem is that employers may not recognise gradual changes in assessment standards and the quality of certification. Universities might slowly drift towards satisfying student demand for credentialling, rather than learning, while continuing to benefit from reputations built up over many years. By the time employers recognise that the qualifications no longer signal what they once did, their value may already be substantially less than it was.

Complementary to credentialling, Potts notes that the decline in junior positions at many employers opens an opportunity for universities. If employers are unwilling to invest in students to train them in what were previously junior roles, universities could step into that gap. In part, universities have already started moving in that direction - witness the rise of 'work-integrated learning', which is now a feature of many universities' offerings, and increasingly compulsory for students (including at Waikato). 

Potts notes that the challenge here will be to find a financing mechanism. However, to me there is an obvious solution. Traditionally, junior roles have bridged the gap between university study and the point at which employers see graduates as sufficiently experienced and skilled to be fully productive. Employers have effectively funded that transition through on-the-job training. If employers are increasingly unwilling to employ graduates in these junior roles, universities could provide that bridge instead, through postgraduate qualifications involving work-integrated learning, either with an employer or in a 'simulated' work environment. Employers may not be willing to pay for this (after all, they are increasingly unwilling to employ juniors), but students may, especially if it smooths their pathway into employment. Again, this might not be something that we like, but it may be the reality in the future.

What about research? Potts has some interesting things to say about that as well, in particular noting that the minimum efficient scale for research is declining and fast approaching one. What would previously have required a large lab team or research group, can increasingly be undertaken by a sufficiently motivated researcher working with agentic AI. Of course, Potts is overstating the case here, because there remains a lot of research that relies on expensive infrastructure, specialised equipment, labour-intensive fieldwork, or collaboration among researchers. However, Potts' view suggests a fundamentally different relationship between research-intensive academic staff and universities. Specifically, Potts proposes that universities:

...unbundle affiliation from employment, making the university more like a club... Rather than the binary of tenured staff or nothing, universities can offer graded affiliation over institutional standing, grant-holding capacity, facility access, and the right to publish under institutional imprimatur, priced as a service and subject to quality review. This converts a fixed cost into a revenue line, retains scholars the institution could not otherwise afford to employ, and extends the warrant into the market for independent research.

This may work for high-profile researchers who can maintain an ongoing flow of external research grant money. However, most researchers are not in that privileged position. Also, Potts ignores that research funding is under pressure, and his proposal would make researchers even more beholden to grant funders than they are now. And it would create an even starker division among academic staff, between those who are highly-research active, bringing in substantial external research funding, and maintaining independence from the institution they are currently affiliated with, and those who are reliant on a role that simply certifies students. This problem is going to affect disciplines to different extents. For example, in a research environment like New Zealand at the moment, where funding changes have severely undercut research funding in humanities and the social sciences, this would be particularly problematic.

Potts also notes that the type of research that universities should specialise in should change:

AI has commoditized knowledge and particularly its search and discovery, including in domains that were previously high in expertise and difficulty such as literature review, statistical analysis and theorem proving. The implication is that the comparative advantage of universities has moved further afield, into the harder to conceptualize and reach corners of new ideas and possibility.

However, that proposal really relies on the willingness of research funders to fund frontier research. That might work in the US, but New Zealand's current research funding environment is increasingly emphasising economic benefits rather than fundamental research. That may make it more difficult to obtain funding for speculative frontier research, where economic benefits are uncertain.

Potts' working paper has a lot of other proposals for universities. However, I think the most important ideas are those I have outlined above. Potts is clear that not all universities are equally affected by generative AI. In particular, he notes that the elite universities are somewhat insulated from changes by their reputations. However, the vast majority of universities are not elite institutions. Potts' vision of universities is radically different from the status quo. We might buy into his vision, or not. But regardless, the economics of higher education are changing, and universities will need to adapt. Whether that requires moving away from the traditional model that combines teaching, credentialling, and research, remains to be seen.

Read more:

• Some notes on generative AI in higher education
• Some notes on generative AI and assessment (in higher education)
• More notes on generative AI and teaching (in higher education)
• Universities' (and teachers') cheap talk on generative AI and assessment
• Simas Kucinskas on AI, university education, and the 'mushy middle'
• The wicked problem of generative AI and assessment
• Matthew Kahn on generative AI and the economics major
• David Deming on generative AI and commitment to learning, and the impact of generative AI on signalling in education

Try this: Econvitals

Over the weekend, Jason Furman (Chair of the Council of Economic Advisors for Barack Obama, and now at Harvard) released an interactive dashboard of US economic data, Econvitals. The site neatly summarises the key economic data for the US, and if you click on any of the headline indicators, it takes you into a page with more detailed information.

In addition to the dashboard at the top, the front page of the site summarises the economy in four questions: (1) Is inflation coming back to 2%; (2) Is the labour market weakening; (3) Is demand too strong or too weak; and (4) Why have long-term interest rates risen. Presumably, Furman (and his team) will update those questions to reflect current priorities as they change.

However, if you scroll down to the next section below that one, the highlight for me was the section, How is the Economy Doing, and How Surprising Has It Been? That compares composite indicators of economic activity and inflation relative to past performance and to forecasts. I can see a lot of use for such a tool.

According to this thread on X, Furman vibe-coded the bulk of the site earlier this year. It now has over 500,000 lines of code sitting behind it (and it shows).

Econvitals has gotten me quite excited about whether something similar could be done for New Zealand data. Of course, we don't have the depth of data or the regularity of data releases that the US does, but nevertheless it would be great to have an interactive dashboard like this that collates all of the key data in one place. I immediately reached out to my colleague Michael Ryan, and we are both keen on this. If you want to be involved, drop Michael or I an email.

In the meantime, Econvitals shows what is possible with US data. Enjoy!

[HT: Marginal Revolution and The Dangerous Economist]

Book review: The Shortest History of Economics

I've read a number of Andrew Leigh books, and generally enjoyed them (see reviews here, and here, and here). So, I was looking forward to reading his 2024 book The Shortest History of Economics. Sadly, I don't think this book was up to Leigh's usually high standards. The reason I say that is that this book seems to contain a lot of material recycled from his other 2024 book How Economics Explains the World. I only realised this after noticing a quirky example Leigh uses to introduce the idea of rival goods - giving someone apples, compared with juggling the apples. I enjoyed the quirky nature of that example in the other book, and used it in one of my classes. However, I didn't need to see it again.

Having said that, there is plenty of new material in this book that doesn't repeat from the other. Leigh takes us through a quick tour of both economic history and the history of economic thought, from humanity's migration out of Africa and the adoption of agriculture, all the way to the coronavirus pandemic. Leigh even finds space (within the 'shortest' history) to consider the future, in particular drawing attention to rising inequality, and the risks from artificial intelligence and climate change.

Given Leigh's role as a Member of Parliament in Australia, I thought this passage was interesting:

One way to think about the role of government is as a risk manager: providing social insurance against risks as diverse as earthquakes, diseases and recessions.

I wonder if, once his political career is over, Leigh will provide us with more thoughts on the role of government. I also learned some interesting facts from the book, such as these government responses to the Great Depression:

Policies were tightened further still in the 1930s. Australia imposed an immigration fee equivalent to one-quarter of the average annual Australian wage. Thailand introduced a literacy test and expensive residence permits. New Zealand closed its Department of Immigration. Migration from Europe was lower in the 1930s than it had been in the mid-1800s.

I was sceptical, but it turns out that New Zealand really did close its Department of Immigration. However, that is not the full story. Parliamentary papers note that the immigration functions were transferred to the Department of Labour (see page 29 here).

Overall, and despite my gripes, this is a good book. If you haven't read How Economics Explains the World, then this book is a good alternative. Given the overlap between the two, I wouldn't recommend reading both, unless you are keen on receiving a good dose of Andrew Leigh. And if you have to choose one, I prefer the other book rather than this one.

This week in research #149

Here's what caught my eye in research over the past week (a very quiet week, it seems, but I've been busy with final assessments in the papers I teach):

• Huang et al. present HouseholdBench, a new evaluation for LLMs that tests whether they can predict behaviour, including how households adjust to changes in various policies

Also new from the Waikato working papers series:

• Suzuki et al. examine the impact of digital water-quality monitors and a farm-management app through a ten-month experiment involving shrimp farmers in Southern Vietnam, and find that the intervention reduces disease outbreaks as well as increasing revenue and profit for these smallholder farmers

The welfare impacts of a positive (consumption) externality

Back in 2023, I wrote a post about the welfare impacts of a negative consumption externality. I think I always meant to follow up that post with a companion post about the welfare impacts of a positive consumption externality, but I never did. So here goes!

To recap, an externality is the uncompensated impact of the actions of one person on the wellbeing of a third party. A positive externality makes the third party better off, while a negative externality makes the third party worse off. They are called externalities because they lie outside the original decision, i.e. some of the costs or benefits are external to the person whose action creates them.

There are fewer examples of positive externalities that come readily to mind than negative externalities, but let me use the example from my ECONS102 class: vaccinations. A person who gets vaccinated receives some benefit themselves, because they are less likely to get infected by whatever they have been vaccinated against. However, they also provide a benefit for other people (including those who are unvaccinated), because other people are less likely to catch the infection as well (because they are less likely to catch it from the vaccinated person).

Consider the market for vaccinations as shown in the diagram below. The market will operate at the quantity where supply (S) meets demand (D) - that is, the quantity traded will be QM, and the price of vaccinations will be PM.

However, the vaccine recipients create a positive externality for other people. Since this externality arises from the buyers of vaccinations, we show this externality on the demand side of the market - we refer to it as a positive consumption externality.[*] This means that the benefits that vaccine recipients receive themselves from getting vaccinated are lower than the benefits that society receives from those vaccinations - the difference is the benefit that is received by other people. We show this on the diagram by differentiating between the marginal social benefit (MSB) and the marginal private benefit (MPB). The MPB is the benefit that vaccine recipients receive for themselves. The MSB is the marginal private benefit, plus the benefit of the externality - the marginal external benefit (MEB).

Now, society prefers the quantity of vaccinations to be the quantity where marginal social benefit (MSB) is equal to marginal social cost (MSC) - I'll explain why a little later in this post. That is the quantity QS, and one way to get to the quantity QS is if the price of vaccinations decreased to PC (because then, more people would be willing to get vaccinated). Notice that in the diagram, relative to the quantity that society prefers (QS), the market produces too little (QM). There are too few vaccinations (or too few people getting vaccinated).

Why does society prefer QS (the quantity where MSB = MSC)? It's because that's the quantity that maximises economic welfare. Economic welfare is the sum of all of the net benefits arising from the market. First, the consumers receive some net benefit from participating in the market. Consumer surplus is the difference between the amount that consumers are willing to pay (shown by the demand curve), and the amount they actually pay (the price). In the diagram, at the equilibrium price and quantity, consumer surplus is the area FEPM. Second, producers receive some net benefit from participating in the market. Producer surplus is the difference between the amount the sellers receive (the price), and their costs (shown by the supply curve). In the diagram, at the equilibrium price and quantity, producer surplus is the area PMEH. Third, the third parties are positively affected by the market. The welfare benefit of the positive externality is the area in-between marginal social benefit and marginal private benefit, up to the quantity of vaccinations received (in this case, QM). That is the area AGEF, and it is added to total welfare. Total welfare is the sum of consumer surplus and producer surplus (which is the area FEH), plus the area of the positive externality (AGEF), and is equal to the area (AGEH).

Now consider the market operating at the quantity QS (with the price PC). The consumer surplus is the area FJPC. The producer surplus is tricky, because to get the market to produce QS, that would require the seller to accept a price that is below their costs (notice that the price PC is below the firms' costs shown by the supply curve). In reality, the government would probably subsidise this good to achieve the optimal outcome, but we will instead assume that the government mandates that the seller provide vaccinations at below cost, to anyone who is willing to pay the price PC (this might make some sense in a public health care system like New Zealand, but is still just a simplifying assumption rather than a description of how this would work in practice). The producer surplus is therefore the area -PCHBJ (it is all negative, because every vaccination is provided for a price that is below the sellers' costs). The area of the positive externality is the area ABJF. Total welfare at QS is equal to ABH.[**] Notice that this total welfare is larger when the quantity is QS than when the quantity is QM. If the market is left alone, there are too few vaccinations (or too few people getting vaccinated), and this decreases total welfare by the area GBE. That area GBE is the deadweight loss of the externality.

Since the market produces too little, a relevant question is how could we get the market to produce more? A Pigovian subsidy (named after 20th Century economist Arthur Pigou) is one option, since subsidies increase the quantity of a good that is traded (for more on that, see this post). Of course, determining the exact optimal quantity QS is difficult in practice. However, we can be sure that it isn't the quantity that is provided by the market.

*****

[*] In contrast, when producers produce a positive externality as a consequence of manufacturing other goods (for example), that is a positive production externality, which we would show on the supply side of the market. An example of a positive production externality is the smell of freshly baked bread emanating from a bakery (at least, it's a positive externality for those who like the smell of bread).

[**] Calculating total welfare involves adding the consumer surplus and externality (adding FJPC and ABJF gives the area ABJPC) and then subtracting the negative producer surplus of PCHBJ, which leaves the area ABH.

Mariana Mortágua on why crypto-assets are not money

Economists typically identify money by the extent to which an asset satisfies three functions. First, it needs to be a medium of exchange, meaning that you give it up when you buy goods or services, and you can receive it when you sell goods or services. Second, it needs to be a unit of account, meaning that you can measure the value of something using the amount of money it is worth. And third, it needs to be a store of value, meaning that you can keep it and expect it to retain a reasonable proportion of its purchasing power into the future. These functions of money go back to William Stanley Jevons in 1875.[*]

There has been a lot of debate about whether crypto-assets such as Bitcoin or stablecoins are money (see the posts linked at the bottom of this post for some examples focused on Bitcoin). This recent article by Mariana Mortágua (ISCTE-University Institute of Lisbon), published in the Cambridge Journal of Economics (sorry, I don't see an ungated version online) wades into the debate, broadening the scope to consider crypto-assets more generally.

Mortágua concludes that crypto-assets are not money. However, unlike much of the debate, she doesn't focus on the volatility of crypto-assets and how that impedes the 'store of value' function. Instead, she adds to the argument that crypto-assets are not a unit of account. Specifically:

Reserves, cash, and bank deposits are titles of debt, claims denominated in one unit of account. Although the issuer of such a claim is not always a State entity (like a central bank), privately issued money is apparently indistinguishable from central bank money because it is issued using the same denomination (the generally accepted unit of account)...

While claiming that cryptocurrencies, like Bitcoin, can become money, their enthusiasts fail to explain how a system of bilateral transactions in one medium of exchange would give rise to a unique unit of account or why people should accept that medium of exchange in the first place...

Indeed, though cryptocurrencies have their own self-attributed trademarks, their prices are denominated in one already established unit of account. Even the most known and utilised cryptocurrency, Bitcoin, is very far from serving as a measure of value of services or assets. On the contrary, Bitcoin is priced in USD, and... only central (political) authorities have the power to determine the ‘meaning of a dollar’.

So, because we don't measure the value of things in terms of crypto-assets, they aren't money. It isn't enough that you can buy or sell things using crypto-assets like Bitcoin. That is an example of a crypto-asset serving the function of a medium of exchange. The transactions are priced in dollars (or other currency), which means that the unit of account is not the crypto-asset, but the currency.

The argument that cryptocurrencies like Bitcoin are not a unit of account is not new (see the links at the end of this post for more). What Mortágua adds is a broader theoretical argument about the nature of money itself, based on the idea that money represents claims denominated in an established unit of account (like dollars). She argues that this distinction is important not just for Bitcoin, but for crypto-assets more generally.

There is, however, an interesting complication here. The dollars in your bank account don't have their own unit of account either. A bank deposit is a claim on the bank, denominated in dollars, but we still count bank deposits as money. Mortágua's argument rests on the credit theory of money, which sees money as part of a system of debts and claims, denominated in a commonly accepted unit of account. Bank deposits are widely accepted as money because the banking system allows them to be used to settle debts.

Stablecoins are not as straightforward. While a dollar-backed stablecoin may also represent a claim on its issuer for US dollars, the arrangements supporting that claim are different from those supporting bank deposits. So, the question is whether those stablecoins have the institutional backing and general acceptability of money. Mortágua argues that they don't.

So, it's not just Bitcoin. Mortágua concludes that crypto-assets in general are not money. However, stablecoins show us that the distinction between money and other financial assets is not always straightforward.

*****

[*] Jevons noted a fourth function, a standard of value (a way of valuing debts), which we now consider to be much the same as being a unit of account.

Read more:

• You may be able to buy Coke with Bitcoin, but that still doesn't make Bitcoin money
• No, El Salvador hasn't just turned Bitcoin into money
• Bitcoin still isn't money, because people aren't using it as if it was

Inequality is not robustly associated with lower subjective wellbeing or worse mental health

When I cover inequality with my ECONS102 class, we talk about some of the many problems that have been associated with inequality, such as unequal access to political representation, lower social cohesion, and less interaction between increasingly segregated social groups. I also talk about the theory that links higher inequality with lower economic growth, though noting that the research is far from settled on that issue (see here and here, for example).

In that class, I don't discuss the research linking inequality with lower physical or mental health. That goes back to my objections to the Wilkinson and Pickett book The Spirit Level (which I discussed here). It turns out that might have been a good choice, at least in terms of mental health.

This recent article by Nicolas Sommet (Université de Lausanne) and co-authors, published in the high-profile journal Nature (ungated earlier version here), presents the results of meta-analyses of the relationship between inequality and subjective wellbeing, and inequality and mental health. Meta-analysis is a way of combining the results of many studies to generate an estimate of the overall effect size.

Their meta-analysis of inequality and subjective wellbeing included 152 estimates from 100 different studies, and they found that inequality was not significantly associated with subjective wellbeing. Their results were qualitatively the same whether subjective wellbeing was measured as life satisfaction or happiness, and did not depend on study quality. That is worth noting, given that about 80 percent of the studies included in the two meta-analyses were judged to be at high or very high risk of bias.

Sommet et al.'s meta-analysis of inequality and mental health included 99 estimates from 75 different studies, and they found a small negative association between inequality and mental health. Specifically:

A 0.05-point increase in the Gini coefficient predicted a 4% (1/0.961) increase in the odds of experiencing a mental-health issue.

The Gini coefficient measures inequality on a scale of 0 to 1, and a 0.05-point increase is similar to the difference between New Zealand (0.326, above the OECD average) and Denmark (0.276, close to the lowest inequality among OECD countries - see Figure 6.1 in this report). Similar to subjective wellbeing, the results were qualitatively the same whether mental health was measured as depression or 'other mental disorders', and did not depend on study quality. The effect was larger when inequality was measured at the 'macro level', meaning the country level (or large subnational areas with populations over one million), although the effect was also statistically significant when inequality was measured at the 'meso level' (small subnational areas, such as counties).

Not so fast, though. After correcting for publication bias (the tendency for statistically significant results to be more likely to be published), Sommet et al. find that, for mental health:

The median bias-corrected estimate was OR+0.05 = 1.018 (0.986–1.051), P = 0.266, thereby confirming the lack of a significant association after correcting for publication bias.

Moreover, Sommet et al. conduct a specification-curve analysis, which tests how sensitive the results are to different modelling choices. They find that their conclusions are robust across approximately 95 percent of 768 alternative model specifications.

The overall result is that inequality is not statistically significantly associated with either subjective wellbeing or mental health, at least not robustly so. However, Sommet et al. then dig a little further, finding some interesting differences in the estimated effects across countries with different characteristics. First, for mental health, they find that the negative association is significant in lower-income samples, but not in higher-income samples. They replicate this result using data from the Gallup World Poll, using average national income to distinguish between lower- and higher-income countries. They explain this as suggesting that:

...inequality may be particularly harmful to low-income populations, possibly by undermining community cohesion... fostering adverse social comparison... or fuelling perceptions of unfairness...

Second, for subjective wellbeing they find that there is a negative association with inequality in high-inflation countries, but a positive association with inequality in low-inflation countries. They also replicate this finding using data from the Gallup World Poll. They explain this in the following way:

Rising costs of living drive distress... and inequality may exacerbate the burden of inflation by increasing social discontent and resentment. Conversely, when prices are stable, inequality might be interpreted as a signal of future economic mobility and foster hope... That is, inequality may only harm well-being under degraded economic conditions (here, high inflation) and have positive effects under others (low inflation).

Their explanations are speculative, and I would be cautious about over-interpreting the mechanisms underlying these latter results. Nevertheless, the fact that both findings were replicated using the Gallup World Poll should provide some comfort about the results. Overall, the results of this meta-analysis can be taken to mean that, while there may be a relationship among some subgroups or some countries at some points in time, there is not a robust overall association between inequality and lower subjective wellbeing or worse mental health.

[HT: Marginal Revolution, last year]

Two papers on inequality in pre-industrial times, that extend our view over two millennia

Back in 2015, I wrote a post about some research by Branko Milanovic (City University of New York) on inequality in ancient times. Milanovic is a prolific inequality researcher, but I only recently got time to read this 2019 article of his, published in the journal Review of Income and Wealth (ungated version here). The article is something of a sequel to the earlier article, tracking changes in income inequality from 14 to 700 C.E. in Europe and the Mediterranean.

This involves combining data from a variety of other studies, first to estimate changes in mean income, then overall and regional inequality, before turning to individual inequality. The resulting estimates are summarised in Figure 2 from the paper, which shows upper and lower bounds on the estimated Gini index:

The 'social table' referred to in Figure 2 comes from this earlier paper by Scheidel and Friesen, with an estimated Gini index of 41.3. The decline from there to a Gini index of between 3 and 13 represents both an unprecedented decline, and an extraordinarily low level of inequality by 700 C.E. Milanovic notes the apparent disconnect between the low inequality he estimates and the impressions of contemporary accounts of poverty, and explains that:

We believe that it is possible to reconcile the conjectured low measured inequality with the impression of a high post-300 inequality because the latter may, in reality, refer to the speculation (or a fact) that the elite was appropriating proportionally much more of the surplus above subsistence than before... Those who observed poverty of most contrasted to the wealth of the very few tended to conclude that inequality must have unambiguously risen. But they might have failed to account for the fact that the numerous poor were now all equally impoverished, and that measured inequality (calculated across all individuals) could not, on that account alone, have been very high...

It is worth noting that these low estimates depend on an assumption that average incomes were barely above subsistence. When almost everyone is extremely poor, even a rich elite that appropriates most of the surplus above subsistence might not produce a particularly high overall Gini index. So, we should be careful that we don't mistake low inequality for prosperity, because it certainly isn't implying that. Nevertheless, the results remain somewhat extraordinary, and it will be interesting to see other estimates of inequality during that time, taking advantage of different data sources or methods.

Milanovic's paper finishes with 700 C.E. What happened to inequality after that? This 2021 article by Guido Alfani (Bocconi University), published in the Journal of Economic Literature (ungated earlier version here), picks up the story in 1300 C.E. and takes it through until the end of the 18th Century, synthesising estimates from a number of other studies of inequality in various parts of Europe. Alfani makes note of Milanovic's results (from the paper I noted above), but doesn't really critically appraise them, which is fine because Alfani is more interested in the period from the late Middle Ages to the early modern period. The studies he synthesises are summarised in Figure 2 Panel A from his paper, which again focuses on the measures of the Gini index, although note that this is for wealth inequality (rather than income inequality):

Alfani uses this to suggest that the evidence supports two stylised facts about inequality in this period: (1) that inequality in Europe increased from 1450 or 1500 until 1800; and (2) before 1450, there was a period of declining inequality that coincides with the Black Death. The decreasing inequality during the Black Death is consistent with an explanation that my ECONS102 students will be familiar with - agricultural wages increased dramatically during that period, alongside a decrease in land rents (as discussed in the book The Rise of the Western World by North and Thomas, which I reviewed here). Alfani then goes on to explore reasons that might explain the overall increase in inequality. He dismisses economic growth as the main explanation, noting that inequality appeared to increase even during periods of economic decline (aside from during the Black Death). Instead, Alfani points to the importance of institutions, and in particular the rise of the fiscal-military state, alongside generally regressive taxation, as well as inheritance systems that protected wealth within families. He concludes that:

The different, and sometimes partly contrasting, explanatory factors for long-term inequality change discussed above lead to a clear conclusion, which can be summarized as follows:

(i) In preindustrial times, there was no necessary cause of inequality growth (like for example, economic growth).

(ii) There were instead a number of sufficient causes of inequality growth, among which we could name economic growth, demographic factors (including urbanization growth), institutional change and politics, “proletarianization” (i.e., the crisis of small ownership), and so on. When one or more of these potential causes became active, inequality grew.

(iii) The bottom line being, in the long preindustrial period it was seemingly much easier for inequality to grow than to decline—and indeed, we have very few examples of long-lasting inequality decline, and almost all of them can be connected to large-scale catastrophes.

Finally, Alfani takes a longer view of wealth inequality through to modern times, by comparing his estimates (based on an average of the various studies included in his synthesis) to those of Thomas Piketty, which begin in 1800. The results are shown in Figure 8 in the paper, which shows the share of wealth held by the richest 10 percent, rather than the Gini index:

So, now we are getting a longer-term picture of nearly two millennia of changes in inequality in Europe, albeit with some important gaps. Milanovic's results suggest that income inequality declined dramatically after the fall of Rome, while Alfani's results suggest that wealth inequality declined around the time of the Black Death, before increasing through to the early 20th Century. In the 20th Century, wealth inequality saw another period of rapid decline (but nothing like the decline in income inequality after the fall of Rome), before increasing again in the second half of the century. Of course, income inequality and wealth inequality are not directly comparable, even if they tend to be correlated. So we should be cautious about drawing conclusions about the overall trajectories of both types of inequality. More studies are needed to confirm these long-run patterns, as well as to fill in the gaps and to extend these results to consider other regions outside of Europe.

Read more:

• Inequality was similar, or higher, in ancient times than it is today
• The last two centuries of global inequality
• Changing global inequality, migration and open borders
• More evidence of declining global inequality
• Chinese growth and global inequality
• Several research articles on inequality
• The global inequality boomerang

This week in research #148

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

• Greenaway‐McGrevy and So (open access) find that upzoning reform in Auckland decreased rents by 23 percent eight years after the reform
• Hess and Thomas (open access) find that state-level recreational marijuana legalisation in the US was associated with near‐zero initial college enrolment growth in STEM majors as out-of-state enrolments increased but were offset by a decrease in in-state enrolment, but that total enrolment increased in later periods as out‐of‐state gains persisted and in‐state enrolment returned to baseline
• Stephenson finds that the 2015-2018 NFL drafts had negligible effects on room rentals and hotel revenue in the host city, while the 2019-2024 drafts (excluding 2020 and 2021) had statistically significant but small effects
• Hillman (with ungated earlier version here) finds no lasting or statistically significant effects of unilateral‐no‐fault divorce laws on divorce rates, using state-level data in the US

Non-market valuation, and the economic value of birdsong

When economists want to know the value of something, we typically look at the price it is traded for in a market. That works well when you want to know the value of an apple, a haircut, or a rare Pokémon card. It doesn't work at all for goods and services that are not traded in markets (like clean air) or those that are not traded because they don't yet exist (like vaccines for HIV). To value those goods and services, we need to apply non-market valuation.

I've previously written several posts about the contingent valuation method and its problems (most recently here), but it is one of several methods that are used for non-market valuation. A similar method is to run a discrete choice experiment, where survey participants are presented with a series of choices between different alternatives that have different characteristics, and are asked to choose the alternative they prefer. By getting participants to make a number of choices between different alternatives, researchers can establish how important each of the characteristics are to the participants. And if one of the characteristics is a price, then researchers can estimate how much participants are, on average, willing to pay for each characteristic.

One of the issues with the contingent valuation method is that it is vulnerable to context. For example, in this 2015 post I describe research that shows that research participants are less rational in their choices if they complete the task late at night. However, researchers may also be able to use context to improve their estimates of what people are willing to pay.

Consider this new article by Anthony Higney (University of Glasgow) and co-authors, published in the journal Ecological Economics (open access). They use a discrete choice experiment, conducted in an online sample of 1094 people from the UK, to measure willingness to pay for ecological restoration. Specifically, they presented each research participant with a series of choices about habitat restoration for birds, where the characteristics of each alternative included the habitat type (woodland, grassland, or arable farmland), the size of bird habitat (compared with baseline), and the quality of the habitat (in terms of the number of bird species), as well as a one-time donation amount. The inclusion of the donation amount means that Higney et al. can use the results to estimate the willingness-to-pay for each of the other characteristics.

Research participants were randomised so that half of the participants had access to a sound recording of birdsong for each of the alternatives (the treatment group), while the other half had no birdsong (the control group). Higney et al. essentially argue that listening to birdsong increases the salience of the habitat quality in the participants' decisions about which alternative to choose. They found that:

...the treatment of playing birdsong causes respondents to be less sensitive to costs in making their choices and rate quality changes from the status quo more highly.

Higney et al. can't tell whether their respondents actually listened to the birdsong or not. So, what their results really say is that respondents who had access to birdsong were willing to pay more for habitat restoration than respondents who did not have access. How much more? The willingness-to-pay for medium-quality habitat (compared with low-quality) was £276.5 higher for those with access to birdsong than those without access, while the willingness-to-pay for high-quality habitat (compared with low-quality) was £274.6 higher. That compares with a baseline willingness-to-pay in the control group of £99.5 for medium quality, and £86.0 for high quality. Interestingly, much of the difference arose because respondents who had access to birdsong were less sensitive to the donation amount, rather than simply placing greater value on improvements in habitat quality.

One reason for caution about the size of these estimates is that the highest donation amount participants were offered in the experiment was £200, which is smaller than the estimated willingness-to-pay for medium-quality and high-quality habitat among the treatment group. So, participants were never actually asked to consider donation amounts as high as their estimated willingness-to-pay. This is not an unusual outcome in this sort of hypothetical exercise, but some follow-up research using a wider range of donation amounts would be useful.

Overall, there was no significant difference in willingness-to-pay between the medium-quality and high-quality habitats (either for the treatment or the control group), and Higney et al. don't have a good explanation for why people weren't sensitive to the quality of habitat restoration beyond medium quality. That would also be a good avenue for future research.

Why would listening to birdsong change what people are willing to pay for habitat restoration? Higney et al. suggest that this could be due to framing:

Listening to the sound clips might have reduced the use of cognitive shortcuts in the treatment group. This could include negativity bias, i.e. giving disproportionate weight to negative information such as the payment vehicle...

Alternatively, respondents in the treatment group may consciously place less weight on other aspects of their choice because the sounds make them feel more connected to nature.

Framing does seem like an obvious explanation, and Higney et al. show in a follow-up regression analysis that participants who had access to birdsong reported significantly greater feelings of connection to nature. That is consistent with the addition of the sound clip meaningfully changing how the alternatives are interpreted. Higney et al. intentionally created sound clips to match the quality of the habitat they were asking about. It would have been interesting to run the same exercise, but with every sound clip being the same (or very similar) generic level of birdsong. Or to have two treatments: (1) generic birdsong; and (2) birdsong that conveys the quality of habitat. That may have helped tease out whether the participants were receiving better information about the alternatives by having access to the sound clip, or whether they were simply feeling more connected to nature after hearing the birdsong.

When valuing the benefits of ecological restoration, we can't rely on market prices and so non-market valuation techniques are really important. The way that we undertake those non-market valuation exercises is important, and understanding how framing affects people's decisions in discrete choice experiments is important for designing research that will give us the most accurate assessment of value. This research can't tell us whether playing birdsong to research participants gives us estimated willingness-to-pay that is closer to the 'true' value. But it does tell us that the information that research participants are presented with, and how that information is presented, matters.

Nobel Prize for Robert Hall and Michael Woodford

New Keynesian economics came into its own in the wake of the Global Financial Crisis, and has provides the playbook for central banks' monetary policy decisions. So, it was fitting to see New Keynesianism recognised in the latest Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel (aka the Nobel Prize in Economics), awarded to Robert Hall (Stanford University) and Michael Woodford (Columbia University), "for microeconomic foundations of macroeconomic stabilisation".

The Nobel Prize Committee's citation noted:

Robert E. Hall and Michael Woodford are awarded this year’s prize in economic sciences for demonstrating how economic models could be reconstructed to revitalise stabilisation policy. Rather than basing the models on statistical links between macroeconomic variables, such as inflation, production and consumption, they chose to build them on a microeconomic foundation. This encompasses an understanding for how the total mass of individual households and businesses affect the economy through their actions.

Because I almost exclusively teach microeconomics, my students wouldn't necessarily recognise their work. However, the macroeconomics topics in ECONS101 include consumption functions as well as the role of expectations, both of which are key features of New Keynesian economics.

This year's award appears to have been very positively received, although as I noted in this post from last week, the prediction markets had different favourites in mind (as did I). Marginal Revolution has a series of posts (see here and here from Tyler Cowen, and here and here from Alex Tabarrok), and there is a good writeup in the Financial Times, especially noting the disconnect between Hall and Woodford's work and the practice of the current Fed Chair, Kevin Warsh.

As always, the Nobel Committee provided some excellent commentary in the form of a popular science summary of Hall and Woodford's work. That is well worth a read if you are not familiar with them.

Book review: Not in Narrow Seas

It's been a while since my last book review, back in August. And that's because I read a couple of novels, and after that I've been working my way through Brian Easton's Not in Narrow Seas. Regular readers of this blog will know that I have a soft spot for economic history, and Easton sets out to provide an in-depth account of the economic history of New Zealand. He justifies his approach as follows:

Many historians have told the Aotearoa New Zealand story before; but an economist uses a particular lens and, I would argue, helps us to see our nation's history in a new way.

Easton won't get any disagreement from me on that point. However, despite the prominence that Easton accords the economic lens, I found that the book fell short on that point in two ways. First, the book is heavily focused on political history, rather than economic history. Now, I will grant that it can be difficult to disentangle the political from the economic. However, a great number of words in this book are devoted to cataloguing national electoral results and the shifting political climate, which makes the economics seem somewhat of secondary importance. I would have preferred more emphasis on explaining the economic mechanisms underlying New Zealand's history and development, rather than recounting the political decisions and events that accompanied it. The core economic history context is actually available in a concise form in the Appendix, which I thought was actually one of the highlights of the book!

Second, when economic theory or technical detail does make an appearance, as opposed to the purely descriptive economic statistics that tend to suffuse books on economic history, that theory or technical detail is sometimes insufficiently explained to the reader. Consider this, on measuring the political position of government after the introduction of MMP:

The first coalition was about 0.8 standard deviations to the right of the New Zealand centre on a left-right scale and the second about the same distance to the left. That means that only 15 percent of the population were on the right of the first coalition or on the left of the second. On the social conservative-progressive scale the first coalition was also about 0.85SDs on the conservative side, while the second was a slightly closer 0.65SDs on the progressive side, with about 25 percent more progressive. The sharp change in the politics of the government did not reflect a sharp change in the politics of the voters.

None of that passage is really explained to the reader, and we are left trying to figure it out for ourselves. The book is not footnoted, but there are references at the end of each chapter. Except for the chapter that quoted passage comes from, where there are no sources provided. Easton is usually much more careful than this, so I was surprised. And it wasn't the only example of sometimes quite technical aspects of economic or political theory that were not really explained. Perhaps in a book that is already over 600 pages, there isn't room for detailed explanations? I was disappointed.

Having said that, there is a lot to learn from the book. I have to admit being under-informed about the political history of New Zealand prior to Muldoon, so despite my gripe above about the political content, it actually turned out that I appreciated that aspect of the book. There is also good coverage of early New Zealand, including pre-European times and the early encounters between Pākehā and Māori. I also learned some surprising facts, such as that New Zealand's first liquor 'laws' date to an 1833 agreement between grog sellers, brokered by British Resident James Busby. Or that New Zealand's first Labour Day celebrations, in 1890, marked the first anniversary of the formation of the Maritime Council, a union that would go on to suffer a crushing defeat in the Great Maritime Strike later that year. And this:

Wellington, which in 1843 was so much of the doldrums that one visitor described it as 'rotten to the core', was saved by sheep.

I won't forget that sentence in a hurry. So. Many. Jokes.

It takes a lot of commitment to write a 600-plus page book on economic history, and to do so in such a way that you keep the reader's attention to the end. Easton is to be commended on that, and I found Easton's writing considerably more engaging than Gary Hawke's The Making of New Zealand (which I have never reviewed, as I read it before I started blogging). I'm not sure that this book is "The" economic history of New Zealand, as the subtitle claims. But it is certainly "A" history of New Zealand, with an economic flavour, and worth reading if you have an interest in that area.