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.

Job fatality and injury risk, and compensating differentials

Last week, my ECONS102 class covered health economics. Part of that topic involves discussion of approaches to measuring the 'value of a preventable fatality' (sometimes also called the 'value of a statistical life', or VSL). One of those approaches is the willingness-to-pay (WTP) approach, which involves estimating what people would be willing to pay in order to avoid small increases in risk. One way of applying the WTP approach in practice is to look at revealed preferences, based on people's actual risk avoidance behaviour. And one example that I use in class to illustrate the revealed preference method is to measure compensating differentials which, rather than measuring willingness-to-pay to avoid risk, measure workers' willingness-to-accept additional risk.

Compensating differentials recognise that jobs that differ in non-monetary characteristics may offer different wages. For example, consider two jobs that are similar in terms of skill requirements. If one of the jobs has positive non-monetary characteristics (e.g. it is safe, clean, and engaging), then more people will be willing to do that job. This leads to a higher supply of labour for that job, which leads to lower equilibrium wages. In contrast, if the other job has negative non-monetary characteristics (e.g. it is dangerous, dirty, and boring), then fewer people will be willing to do that job. This leads to a lower supply of labour for that job, which leads to higher equilibrium wages. The difference in wages between the attractive job that lots of people want to do and the unattractive job that fewer people want to do is called a compensating differential. The compensating differential essentially compensates workers for working in jobs with negative non-monetary characteristics compared with working in jobs with positive non-monetary characteristics.

The risk of being injured on the job is a substantial negative non-monetary characteristic for some jobs. Based on the idea of compensating differentials, workers should only be willing to take on a job with a higher risk of death or injury if that job comes with a higher wage. Measuring that additional wage would provide a measure of the compensating differential, which could then be used to estimate the value of a preventable fatality.

As an example, consider this 2022 article by Zac Reynolds, Daehoon Nahm and Craig MacMillan (all Macquarie University), published in the journal Economic Record (open access). They estimate compensating differentials for job fatality and injury risk in Australia, using income data from the Household, Income and Labour Dynamics in Australia (HILDA) survey from 2010-2018, and risk data from Safe Work Australia. Their regression model estimates the compensating wage differential (CWD) controlling for industry and occupation, as well as for a range of demographic and labour force characteristics of the workers in the sample. In their preferred set of results:

...the CWD and corresponding VSL for fatal risk are 0.13% and $9.7 million respectively.

Interestingly, union members receive significantly higher compensation for fatality risk. In their preferred model, the CWD for union members is 0.44 percent for an increase in fatality risk of one in 100,000 workers, significantly higher than for non-members. If you need a good reason to join a union, this might provide it.

Surprisingly, Reynolds et al. find no evidence of a CWD for non-fatal risk overall. In fact, their preferred results suggest that higher non-fatal injury risk is associated with lower wages, rather than higher wages, for non-union workers. However, alongside that there is a significant CWD for union members, where:

...union members receive a CWD of 0.09% for a unit increase in injury risk from the mean level which corresponds to a VSI of $65,300.

VSI is the 'value of a statistical injury', which is the non-fatal equivalent of the VSL. One limitation of Reynolds et al.'s analysis is the potential for omitted variable bias. Their models include either fatal risk or non-fatal risk, but never both together. Since these risks are positively correlated, omitting one may bias upwards the estimated effect of the other. However, Reynolds et al. explain that including both risks would create multicollinearity problems, meaning that the results would potentially be less biased, but also less precise.

Finally, Reynolds et al. compare their results with the 'official' VSL applied by the Australian government, noting that:

For Australian workers who belong to a union, this estimate implies a VSL of $34.9 million. Ignoring union coverage results in a VSL of $9.7 million. These estimates are both well above the Australian government VSL estimate of $5.1 million...

For comparison, the New Zealand government currently applies a VSL of $12.5 million (in 2021 dollars, which is comparable with the 2021 VSL that Reynolds et al. report for Australia) for measuring the benefits of road safety improvements. This is equivalent to about AU$10 million at the current exchange rate.

The 'value of a preventable fatality' (or value of a statistical life, if you prefer) isn't a price tag on anyone's life, but an estimate based on how people trade off money against small changes in the risk of death. As Reynolds et al. demonstrate, those estimates can vary substantially, which is worth remembering when governments use a single number to represent the benefits of increased safety and in other applications.

Read more:

• The likely consequences of New Zealand's new value of a statistical life

This week in research #147

Here's what caught my eye in research over the past week (a very quiet week, it seems):

• Silva, Sá, and Biscaia (open access) find that a one-standard-deviation increase in high school GPA adds 5.9 first-year ECTS (European Credit Transfer System) at university, versus 1.8 ECTS for a one-standard-deviation increase in standardised national exam score, with the difference larger in the most selective university programs

The economic impact of the 2010 FIFA World Cup

Economic impact studies undertaken in advance of large sporting events typically anticipate large positive economic effects. However, studies undertaken after the event almost universally fail to demonstrate the anticipated effects. To that list (and see the links at the end of this post for specific examples), you can now add the 2010 FIFA World Cup, hosted by South Africa.

This 2025 article by Busani Moyo (University of South Africa) and Tendai Gwatidzo (University of the Witwatersrand), published in the Journal of Sports Economics (open access), provides the details. They apply the synthetic control method, which involves comparing the time series of outcome variables for South Africa with a counterfactual made up of a weighted average of other countries, chosen because that weighted average best replicates the trajectory of the outcome variable from before the treatment date.

In their main analysis, they use 2010 as the treatment date, in order to demonstrate any effect of the staging of the 2010 FIFA World Cup itself. The synthetic control they employ is made up of 31.7 percent Brazil, 21.3 percent Morocco, 18.1 percent Algeria, 15.5 percent Botswana, and small weightings of Kazakhstan, Malaysia, Mauritania, and Argentina. The results are shown in Figure 1 from the paper:

The solid blue line is the actual GDP per capita for South Africa, while the dashed red line is the counterfactual 'synthetic South Africa', which is supposed to show what would have happened to GDP per capita if South Africa hadn't hosted the FIFA World Cup. The results clearly demonstrate that actual South Africa performed worse than synthetic South Africa, suggesting that hosting the FIFA World Cup may have made South Africa worse off (at least, using GDP per capita as a measure).

But wait, not so fast. A lot of economic activity occurs in the lead-up to the hosting of the event. Moyo and Gwatidzo therefore change the treatment date to 2004, when South Africa was announced as the host, and check whether there was any impact then. The synthetic control changes (since they are using a different treatment date), now being made up of 31.3 percent Morocco, 24.7 percent Brazil, 17.9 percent Algeria, 14.3 percent Malaysia, and small weightings of Kazakhstan, Costa Rica, and Mexico. The results from this analysis are shown in Figure 3 from the paper:

Moyo and Gwatidzo interpret this as showing a positive effect on GDP per capita in the lead-up to the event, but I struggle to see evidence of any effect in Figure 3 (at least, before 2010 where, as in Figure 1, actual South Africa performs worse than synthetic South Africa). Actual GDP per capita tracks synthetic GDP per capita remarkably closely between 2004 and 2010, with little indication of any sustained positive effect. Moyo and Gwatidzo don't provide a clear quantitative estimate of the effect, which makes it difficult to assess whether the apparent positive effect is economically meaningful or statistically significant. Both seem unlikely to me.

Moyo and Gwatidzo then turn to looking at tourism arrivals, where the results are mixed. Using 2010 as the treatment date, actual tourism arrivals exceed those for synthetic South Africa for several years after the World Cup. However, using 2004 as the treatment date, synthetic South Africa clearly outperforms actual South Africa.

Moyo and Gwatidzo then turn to looking at tourism arrivals. In this case, the effect is modestly positive, as shown in Figure 10 from the paper:

Notice that in this case, there is a small positive difference in international tourism arrivals between actual South Africa and synthetic South Africa, at least up to 2013 or 2014. So, at least there is some good news for South Africa. Nevertheless, given the apparent negative effects on GDP per capita after the event, I am not convinced by their conclusion that:

These results, at least for South Africa, show that it is not the actual hosting of the World Cup that has a positive effect on GDP but the period of preparation for the tournament. The actual hosting of the tournament is only beneficial in the form of international tourism inflows.

Like many other studies, I think the results suggest that South Africa had a good party, which attracted extra visitors from outside the country, but failed to deliver the large positive economic benefits that were anticipated. I suggest that you file this study along with the multitude of others that cast doubt on the large positive economic benefits that are routinely predicted for major sporting events.

Read more:

• Tim Hazeldine on economic impact studies
• Beware economists bearing impact studies of the America's Cup
• Even less reason to believe in the economic impact of the America's Cup
• The overstated impact of the Super Bowl
• The economic impact of the 2010 World Cup in South Africa
• The economic impact of the 2000 Sydney Olympics
• Maybe hosting the Olympics just shuffles income around a country, rather than increasing it

Modelling the Nobel Prize in Economics

The Nobel Prize in Economics (technically, the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel) will be announced next Monday. Who will win? It is a closely guarded secret, but of course there are prediction markets - at the time of writing, Kalshi has Ariel Pakes as the favourite, with Susan Athey a close second, and Richard Blundell a distant third.

In a discipline where modelling is ubiquitous, it is worth asking whether it is possible to model who will win the Nobel Prize. This recent working paper by Peter Dolton and Richard Tol (both University of Sussex) makes a good attempt. They build a dataset of past winners and candidates (based on research performance as well as winners of other awards), and then estimate a model that shows the factors correlated with winning the Nobel Prize.

Dolton and Tol start with a simple assumption, which is that the Nobel Prize Committee first chooses a field to award the prize to, and then they choose the best candidate within that field. They justify this assumption by showing that there is some regularity in the way that the award goes from field to field over time (based on their categorisation of 14 fields of economics). They use this to construct a 'transition matrix', that records how often the Nobel Prize goes from one field in one year to another the next year. 

Which fields get Nobel Prizes? Dolton and Tol show that larger fields (those with more candidates), those that have waited longer since their last prize win, and the transition matrix, are all statistically significantly correlated with which field wins the prize.

Who wins the Nobel Prize within the winning field? Dolton and Tol find that winning candidates are older (although the relationship with age is non-linear, and peaks at age 70-71 before declining), and those who have had a student already win a Nobel Prize are more likely to win.

Some important things come out of this paper. First, who will win the Nobel Prize in 2026? The 2025 winners were Joel Mokyr, Philippe Aghion, and Peter Howitt. Their research field, according to Dolton and Tol's categorisation, is 'Growth'. According to the transition matrix in Table E.8 of the paper, the most likely field to follow 'Growth', is 'Equilibrium and Welfare' (although 'Development and Economic History' and 'Macro' are also possible). 

I got ChatGPT to comb through Dolton and Tol's candidate list and identify the top candidates in 'Equilibrium and Welfare'. ChatGPT suggested Andreu Mas-Colell or Partha Dasgupta. I have heard Dasgupta's name in people's shortlists before, but not Mas-Colell. Of course, if the Committee considered they had already ticked the 'Economic History' box with Joel Mokyr last year, then next according to the transition matrix would be 'Games and Market Structure', 'Information', or 'Macroeconomics'. Across those fields, ChatGPT suggested many names, but noted that David Kreps is the strongest overall, to which I would add Ariel Pakes (note the consistency with the Kalshi market prediction). ChatGPT rated Oliver Blanchard the top candidate in Macroeconomics. Anyway, we will see next week!

Second, I love the list in Table 4 of the paper, which shows the economists who, according to Dolton and Tol's model, have had the greatest chance of winning the Nobel Prize but have not done so. Top of this list is Michal Kalecki (died in 1970), followed by Lionel Robbins (1984), Jacob Marschak (1977), Arthur Burns (1987), and Frank Hahn (2013). Other notable names in that list (at least, according to me) are Bill Phillips (died in 1975), Harold Hotelling (1983), Bill Baumol (2017), and Henri Theil (2000). There are a few on the list who are still alive, including Tim Besley, Robert Barro, Guido Tabellini, George Loewenstein, and Torsten Persson.

One reason why so many outstanding economists died without receiving the prize is that it was only first awarded in 1969, by which time there was a long backlog of worthy candidates. The third important thing to come out of this paper is a counterfactual exercise, predicting who would have won the Nobel Prize each year if the prize had been first awarded in 1901 (along with the other Nobel Prizes). Table F.15 in the paper has the results (up to 1976, after which it is assumed that the awards would continue as they have been). A number of important names appear in this list, including the first three winners being Leon Walras, Carl Menger, and Francis Edgeworth. My students would no doubt recognise Alfred Marshall (1910), Corrado Gini (1924), Joseph Schumpeter (1930), Arthur Pigou (1935), John Maynard Keynes (1938), and Joan Robinson (1958), among others. Interestingly, in this counterfactual exercise, the first woman to win the prize would have been Beatrice Webb in 1924 (jointly with Sidney Webb and Corrado Gini), some 85 years before Elinor Ostrom.

The Nobel Prize announcement is one of the highlights of my year. Now that I've considered the results from the model, with some assistance from my special adviser ChatGPT, I'm prepared to make my prediction: Kreps and Pakes. We'll find out next week!

[HT: Marginal Revolution]

The beauty premium doesn't seem to extend to billionaires

There is a broad literature supporting the existence of a 'beauty premium' in labour markets - more attractive workers tend to earn more. There are a number of channels proposed to account for this, including confidence, social or communication skills being higher among more attractive workers, attractive workers sorting into occupations that reward attractiveness, and discrimination by employers (or by customers). How does the beauty premium extend to wealth, and in particular to extreme wealth?

In this 2022 article published in the journal Labour Economics (ungated earlier version here), Daniel Hamermesh (University of Texas at Austin) and Andrew Leigh (Australian Member of Parliament) investigate the relationship between attractiveness and wealth among billionaires on the 2008 Forbes billionaires list. The attractiveness ratings for each billionaire are derived from 0-10 scores given by 16 students at Australian National University, based on photos published with the list. Hamermesh and Leigh control for age (and age-squared), education (whether the billionaire was a college graduate), and whether they were male, from a Western country, and inherited their wealth.

In their main analysis, Hamermesh and Leigh find that wealth is not statistically significantly correlated with attractiveness in their billionaires sample. They find the same for education - it is not statistically significantly correlated with wealth in their sample either. They try various alternative specifications, none of which show a significant correlation. Hamermesh and Leigh conclude that their results:

...show that a sample that is taken from the extreme tail of the dependent variable no longer exhibits the systematic patterns seen across the distribution.

They illustrate this further using data from the American Community Survey. While they don't have attractiveness in that data source, they do have education and labour market experience variables. They find that, for the whole sample, both college education and labour market experience are positively and statistically significantly associated with income. However, when they restrict the sample to the 0.1 percent highest earners in the sample, there is no significant correlation between education and income (or between labour market experience and income).

Hamermesh and Leigh use their results to caution us against drawing conclusions from analyses conducted on extreme tails of a distribution, noting that:

Empirical regularities that are common in the general population may not hold up among atypical subsets of the population, whether drawn from the elite (e.g., Olympic athletes, Fortune 500 CEOs) or the most disadvantaged (e.g., prisoners, the homeless). Within these groups, outcomes are more likely to be due to unobservables and pure chance than observable traits.

Coming back to the billionaires, why is there no correlation between attractiveness and wealth? Think about the main mechanisms that are proposed to underlie the beauty premium. More attractive billionaires likely aren't benefiting from employer discrimination, and customer discrimination seems less obviously important. Sorting into different occupations that reward attractiveness is unlikely to explain much of the differences in wealth at the extreme (and if we treat 'billionaire' as an occupation, then arguably they all have the same occupation). That leaves differences in confidence, social or communication skills as more plausible mechanisms. There might be differences between billionaires in those traits, but would they really explain the difference in wealth between Warren Buffett (US$62 billion in 2008) and Larry Ellison (US$25 billion)? Perhaps some further research might uncover the relationship between confidence and wealth among billionaires, but I think it more likely that a lot of it is luck. Buffett even famously said as much.

This research is a useful reminder to pay attention to how a sample has been selected. Relationships that are strong across the population may disappear when we look only at people drawn from the extreme tail of the outcome distribution. That doesn't necessarily mean that attractiveness doesn't matter at all for billionaires, only that we cannot expect the same relationship to hold in such an unusual sample. As F. Scott Fitzgerald wrote about the very rich: "They are different from you and me".

Read more:

• The beauty premium in undergraduate study is small, and more attractive women major in economics
• New results questioning the beauty premium should be treated with caution
• The beauty premium in the LPGA
• The beauty and height premiums in the labour market
• Blonds have more fun (or rather, they get paid more)
• The beauty premium for economists
• More evidence on the blond wage premium
• The beauty premium at the intersection of race and gender
• Narrowing down on the source of the beauty premium
• The beauty premium and student grades for in-person and online education
• Is there a premium for very unattractive workers?

How Alaska is like South Waikato

I've written a couple of times about the challenges that South Waikato faces in attracting workers (see here and here). This problem is not unique to South Waikato - a lot of rural and remote areas face the same challenges. In just the latest example I've seen, Jordan McGillis reports in City Journal on the situation in Alaska:

My analysis of Census data finds that more than 40 percent of civilian, prime-age, noncollege Alaska men employed in blue-collar occupations earn at least $75,000. More strikingly, 9.4 percent of Alaska’s civilian, prime-age, noncollege men work a blue-collar job and earn at least $100,000, compared with 3.6 percent nationally, ranking Alaska first in the country. Among those in such occupations more than one in four earns six figures...

That’s no surprise to [Ray Weber, Dean of technical and vocational education at the University of Alaska Anchorage]. “Can [wages in Alaska] be higher [than in the rest of the country]? Yes, especially Slope jobs or jobs that suck. Like, we have electrical linemen that go across the state. They’re going to Unalakleet, the only way to get there is by airplane. And there’s one pizza joint. One. No other restaurant. You generally end up sleeping either in a bunkhouse, if you’re lucky. Or you’re sleeping in the school auditorium. . . . I like Alaska, but we’re asking the wrong questions if you’re saying, ‘high-paying, lucrative jobs.’ What do the younger generation consider important? The answer is: Things that are not in Alaska.”

Economists call this a compensating wage differential: the premium required to induce workers to accept jobs with undesirable nonpecuniary characteristics. Work in Alaska is colder, darker, lonelier, and often more dangerous. The roughly $120,000 premium earned by North Slope oil-and-gas workers over their counterparts elsewhere, and the roughly $40,000 premium earned by electricians deploying to isolated communities such as Unalakleet, are partly the price employers must pay to fill jobs few want.

Workers with the same skills can often accept lower wages in Texas or Louisiana in exchange for a more attractive lifestyle.

Wages differ for the same job in different firms or locations. Consider the same job in two different locations. If the job in the first location has positive non-monetary characteristics (e.g. it is in an area that has high amenity value, where people like to live), then more people will be willing to do that job. This leads to a higher supply of labour for that job, which leads to lower equilibrium wages. In contrast, if the job in the second location (Alaska, for example) has negative non-monetary characteristics (e.g. it is in an area with lower amenity value, where fewer people like to live, where it is cold, dark, lonely, and more dangerous), then fewer people will be willing to do that job. This leads to a lower supply of labour for that job, which leads to higher equilibrium wages. As McGillis notes, the difference in wages between the attractive job that lots of people want to do and the unattractive job that fewer people want to do is called a compensating differential. The compensating differential essentially compensates workers for working in jobs with negative non-monetary characteristics compared with working in jobs with positive non-monetary characteristics.

And so, jobs that pay less in the rest of the US must pay much more in Alaska in order to attract workers.

Read more:

• Reason to be wary if a job in Taumarunui offers an Auckland salary
• The compensating differential for rural GPs must be enormous
• Compensating differentials are alive and well in Tokoroa
• Rural Australia sets a new high bar for compensating differentials
• The $300,000 job that no one wants
• The South Waikato compensating differential strikes again

This week in research #146

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

• James and Kjorstad find that spelling and grammar errors significantly reduced perceived research quality within a sample of US economists, while manuscripts prepared in LaTeX were generally evaluated more favourably than those prepared in Microsoft Word within an international sample of economists
• Fornwagner (with ungated earlier version here) finds no effect of menopause on women’s risk preferences, using an online sample of over 1700 UK women
• Wang, Wang, and Zhou (with ungated earlier version here) use global air traffic data as instrumental variable to estimate the effect of in-person interactions on bilateral trade, finding that online interactions are an imperfect substitute for in-person contact
• Garthwaite et al. (with ungated earlier version here) find that the rent-sharing between college basketball and football programmes and other sports transfers spending away from students who are more likely to be Black and come from poor neighbourhoods toward students more likely to be White and come from higher-income neighbourhoods