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