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