The Cristiano Ronaldo effect on the Saudi Pro League

This past week, my ECONS102 class covered labour markets. Part of that topic is a discussion of superstar and tournament effects, which are explanations for why, within a particular labour market, some workers get paid a lot while most workers get paid very little. As an example, in the labour market for actors, the top actors get paid a lot, while the 'average' actor barely earns enough to get by (or doesn't earn enough to get by, which is why so many aspiring actors work as waitstaff at restaurants).

Superstar effects arise when the worker (the superstar) earns a lot of value for their employer. Labour markets with superstar effects generally have two features:

1. Scale – the top performers can satisfy the demand of a lot of consumers (which is more likely when the output is non-rival). That generates a very high value (technically, the value of the marginal product of labour, or VMPL) for the employer, with little additional cost; and
2. Non-substitutability – the particular job (or skills, or ‘style’) performed by the top performer is unique, and cannot be easily replicated by the ‘average’ worker.

Because of the high value created by the superstar, and non-substitutability, employers compete fiercely over these top performers, and so they will receive a very high wage. Movie stars provide a good example of the superstar effect in action.

In some labour markets, workers are rewarded for their relative (rather than absolute) performance. In these markets, workers essentially compete for a ‘prize’ – maybe a raise or a promotion, and they only need to be a little bit better than the second-best person in order to ‘win’ the prize. These labour markets are said to exhibit tournament effects. In these markets, the extra pay for the top workers arises not because they create more value for the employer, but as a way of incentivising workers to work hard (in order to try and 'win' the tournament). Good examples of tournament effects in action are the pay for CEOs, sports stars, or hedge fund managers.

How can we distinguish between superstar and tournament effects? Some markets actually have both, like the market for sports stars. However, for the high wages to be a superstar effect, the worker must generate much more value for the employer than alternative workers do. And that brings me to this recent article by Dominik Schreyer (WHU – Otto Beisheim School of Management) and Carl Singleton (University of Stirling), published in the journal Contemporary Economic Policy (open access).

Schreyer and Singleton look at the impact of Cristiano Ronaldo on the Saudi Pro League, after he was surprisingly signed by the Al Nassr club a few days after the 2022 FIFA World Cup. They note the potential for superstar effects here, specifically:

The Ronaldo signing, and subsequent player moves, could attract international tourists and foreign investments to the country, help market the TV product abroad...

Schreyer and Singleton look at the impact of Cristiano Ronaldo on stadium attendance, as a measure of his positive impact on the league. Their data comes from 240 matches played by 16 different clubs during the 2022–2023 season, noting that Ronaldo made his debut for Al Nassr on January 22 and played in 16 matches over the course of the rest of the season. In their preferred regression specification, Schreyer and Singleton find that:

...the estimated average effect of Ronaldo playing at home is 20% points of capacity, and the average effect of him playing away is 15% points, significantly different from zero at the 10% level (two‐sided test).

Moreover, when they look at whether there was a general impact of Ronaldo joining the league on attendance, they find that:

...the post‐Ronaldo‐playing period of the season 2022–2023 was associated with generally higher attendance demand across all matches, by 3% points of stadium capacity, significantly different from zero at the 10% level (two‐sided test), with a further significant 17% point effect when he played at Al‐Nassr's home.

Schreyer and Singleton conclude that this is evidence in favour of a superstar effect. However, I am not entirely convinced. How much additional value is Ronaldo generating for Al Nassr and the Saudi Pro League? Mean stadium capacity in Schreyer and Singleton's sample is just 26,000. So, an increase of 15-20 percentage points is an increase of 4000-5000 people in attendance at the game. That isn't going to generate anywhere near enough additional revenue to cover Ronaldo's salary of €180 million per year. Even with jersey sales and an increase in advertising or sponsorship revenue, this will not break even for Al Nassr. On the other hand, for Saudi Arabia generally this might be a good deal. Schreyer and Singleton note that:

The Ronaldo signing... [could] legitimize KSA's other foreign sports investments, including the 2021 takeover of Newcastle United FC...

Perhaps those broader benefits are worth more than Ronaldo's salary? Saudi Arabia may value the legitimisation quite highly (hence all the accusations of sportswashing). Still, it seems to me that at least part of Ronaldo's salary is a tournament effect. If Al Nassr had signed Kylian Mbappe instead, I'm sure they would have paid a hefty salary for the privilege. Instead, Mbappe is being paid the comparatively pauper-like salary of €36 million at Real Madrid. That suggests that the next best player earns far less than Ronaldo [*], which is indicative of Ronaldo's salary being a tournament effect.

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[*] We could argue endlessly about who are the top and second-best players. However, choose any from this list of the top-paid footballers, and the argument still holds up.

Superstar effects and inequality in the music industry

Following on from Thursday's post on superstar and tournament effects in social media, the Wall Street Journal earlier this year (gated) gave us some good data on superstar effects in the music industry:

A small number of superstars like Beyoncé and Taylor Swift is gobbling up an increasingly outsize share of concert-tour revenues, as music’s biggest acts dominate the business like never before.

Sixty percent of all concert-ticket revenue world-wide went to the top 1% of performers ranked by revenue in 2017, according to an analysis by Alan Krueger, a Princeton University economist. That’s more than double the 26% that the top acts took home in 1982.

Just 5% of artists took home nearly the entire pie: 85% of all live-music revenue, up from 62% about three decades earlier, according to Mr. Krueger’s research. “The middle has dropped out of music, as more consumers gravitate to a smaller number of superstars,” he writes in a new book, “Rockonomics,” set to come out in June. (Mr. Krueger died in March.)"

"Performers’ royalties—for acts big and small—are generally much smaller on streaming than on records, CDs or download sales, so artists have to turn to concert revenue for more of their income. And it’s only the superstars who have the ability to charge significantly more for tickets than their predecessors did a generation ago. That leaves non-superstar performers competing for a shrinking share of the concert pie...

Meanwhile, at the bottom of the industry, the lowest 2,500 acts ranked by revenue grossed an average of about $2,500 in 2017 from concert tickets, out of the 10,808 touring acts that year that Mr. Krueger studied. There were 109 acts in the top 1%."

As I noted in the earlier post, superstar effects occur because top performers are paid (in part) based on the amount of value that they generate. Usually this is the value generated for their employer, but in the case of (self-employed) music stars, it might also be the value they generate for their legions of fans. The more fans whose demand they can satisfy, the more they will earn.

Also readily apparent from the WSJ article quote above, is that superstar effects contribute to inequality. If the top 5% of artists are earning 85% of all live-music revenue, then that represents a pretty high level of inequality. A back-of-the-envelope calculation [*] leads to an estimated Gini coefficient for the music industry of 80. That is much higher than the Gini coefficient for any country - according to the World Bank, South Africa is the most unequal country, with a Gini coefficient of 63.

And note that inequality among music artists has been increasing over time. Of course, superstar effects is only one of many contributors to inequality, and inequality among musicians will be a trivial contributor to overall inequality. However, superstar effects are present in many industries, including art, books, entertainment, and even academia.

Finally, as a side note, I'm really looking forward to reading the late Alan Krueger's book Rockonomics. I trust that it builds substantially on the paper that I discussed in this 2017 post.

[HT: The Dangerous Economist]

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[*] Assuming that the income share of the bottom 95% of the population is 15%, and the income share of the top 5% is 85%, and assuming a kinked-line Lorenz curve, leads to an area under the Lorenz curve of 0.1, and a Gini Coefficient of 0.8. See here for more on how to calculate the Gini coefficient, using a Lorenz curve made up of kinked lines.

Social media influencers and superstar effects

In my ECONS102 class, we talk about why earnings differ between different jobs. However, even within jobs that are ostensibly the same, workers may have different wages. Putting aside the gender wage gap and discrimination, two reasons for differences in wages are superstar effects and tournament effects.

Superstar effects, described by Sherwin Rosen in the 1980s [*], occur because top performers are paid (in part) based on the amount of value that they generate for their employer. If a top performer generates a lot of value, they will be paid more. This explains much of the rise in earnings over time for top sportspeople or entertainers - as television (and more recently internet) viewership has grown, the value generated by a top sportsperson or entertainer (in terms of the number of viewers they attract) has grown, and their salaries or earnings have grown as a result.

Tournament effects, described by Rosen and Ed Lazear in the 1980s, occur when people are paid a 'prize' for their relative performance (that is, for winning the 'tournament'). The prize may take the form of a bonus, a raise, or a promotion. The point is that each worker only needs to be a little bit better than the second best worker in order to 'win' the tournament.

These effects are nicely illustrated in the case of social media influencers, as described in this article in The Conversation by Natalya Saldanha (RMIT University):

As people consume less traditional media and spend more time on social platforms, advertisers are increasingly using these influencers to spruik their products. A mega-influencer like Kylie Jenner, with 139 million followers on Instagram, can reportedly charge more than US$1 million for a single promotional post...

So far most of the indications are that the new economics of influencer marketing are not too different to the old economics of marketing.

As in the acting, modelling or music industry, there’s a tiny A-list of superstar influencers making millions. Then there’s a somewhat larger B-list making a handsome living. But the vast bulk of influencers would be better off getting an ordinary job.

In 2018 a professor at the Offenburg University of Applied Sciences in Germany, Mathias Bärtl, published a statistical analysis of YouTube channels, uploads and views over a decade. His results showed that 85% of traffic went to just 3% of channels, and that 96.5% of YouTubers wouldn’t make enough money to reach the US federal poverty line (US$12,140, or about A$17,900).

There are elements of both superstar effects and tournament effects here. If an influencer promoting a product can increase sales, then it makes sense that they will be paid more if they have more followers. So, a superstar with millions of followers will be paid substantially more than one with just hundreds or thousands.

And, influencers are competing for a scarce advertising spend, where successful influencers will attract paid work from many willing advertisers. Being slightly better than the second best influencer is likely to result in a disproportionate number of advertising contracts, increasing their earnings by a lot (and 'winning' the tournament). In contrast, slightly less successful influencers could end up earning less than the poverty line. This probably plays out separately in 'markets' for influencers with a broad appeal, and those whose followers are in a particular niche that advertisers want to target. Interestingly, the tournament effects here are little different to the effects for drug dealers, as Steven Levitt and Stephen Dubner describe in the excellent book Freakonomics (also described in this LA Times article from 2005).

Being a social media influencer isn't going to be a path to riches for the majority of aspiring wannabe Kylie Jenners. The best advice might be to try and exploit a very particular niche audience that advertisers are seeking and one that is not already occupied by one (or many) successful influencers. However, most of these wannabes are going to need a day job.

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[*] This was not a new insight, as Alfred Marshall had made a similar point as early as 1875.

Superstar effects, the Black Caps and the Silver Ferns

New Zealand has seen a bit of team sporting success over the last two weeks, with the Black Caps runners-up in the Cricket World Cup, then the Silver Ferns winning the Netball World Cup. However, the two successes were not equally remunerated, and this has created a bit of a media storm today, after it was revealed that the Black Caps will share $3 million in prize money for their second place, while the Silver Ferns receive nothing. On the surface, that seems grossly unfair, and the head of the International Netball Federation was interviewed on Radio New Zealand on that point. However, it isn't clear what the INF could possible do to address the situation.

In my ECONS102 class today, we discussed one reason that explains the discrepancy in prize money between men's cricket and women's netball - 'superstar effects' in labour markets. In the early 1980s, Sherwin Rosen identified that some workers earn much more than their peers do, or more than similar workers did in the past. [*] The reason is that top performers are paid (in part) based on the amount of value that they generate for their employer (e.g. for the national or international sporting body). If a top performer generates a lot of value, they will be paid more. This explains much of the rise in salaries over time for top sportspeople - as television (and more recently internet) viewership has grown, the value generated by a top sportsperson (in terms of the number of viewers they attract) has grown, and their salaries have grown as a result.

Cricket is a sport with a massive global following, albeit concentrated in a small number of former British colonies. The ICC has revenues in excess of US$1.5 billion per year, which is shared among the member countries. In contrast, the International Netball Federation barely breaks even, with revenues of around US$1 million per year (see page 15 of this report). The cricket players clearly generate much more value for the ICC, than the netball players generate for the INF. And they receive compensation that is commensurate with the value they generate.

The comparison across two very different sports, with different levels of spectator and sponsorship support, is clearly problematic and should be avoided. If your goal is to highlight gender differences in remuneration, it would be much better to instead concentrate on the disparities within given sports, such as the difference in prize money between men's and women's FIFA World Cups. Superstar effects can still explain this difference, but at least then you could argue for a redistribution or equalisation of incomes within a single sporting body, rather than arguing that a relatively poor sporting body should be able to match the prize money paid by a much wealthier organisation.

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[*] This was not a new insight, as Alfred Marshall had made a similar point as early as 1875.

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• Footballer earnings, superstar and tournament effects

It seems it's better to publish economics papers with U.S. data

This post follows on from yesterday's post about the length of article titles, which showed a strong negative correlation between title length and research quality (so papers with shorter titles were more likely to be published in better journals, and attracted more citations). There are, of course, a lot of other factors that affect where journal articles get published. One gripe for many researchers outside the U.S. or the U.K. is how hard it is to get published in top journals using data from outside the U.S. or the U.K. Until relatively recently, that gripe was based on purely anecdotal evidence. However, a 2013 article by Jishnu Das, Quy-Toan Do, Karen Shaines (all from the World Bank), and Sowmya Srikant (FI Consulting), published in the Journal of Development Economics (ungated version here) provides some empirical evidence on this.

Das et al. use data on over 76,000 papers published in 202 economics journals over the period from 1985 to 2005, and the disparity in data sources for published economics papers is clear:

Over the 20 year span of the data, there were 4 empirical economics papers on Burundi, 9 on Cambodia and 27 on Mali. This compares to the 37,000 or so empirical economics papers published on the U.S. over the same time-period. This variation is also reflected among the highly selective top-tier general interest journals (henceforth top-tier journals) of the economics profession (American Economic Review, Econometrica, The Journal of Political Economy, The Quarterly Journal of Economics and The Review of Economic Studies). American Economic Review has published one paper on India (on average) every 2 years and one paper on Thailand every 20 years. The first-tier journals together published 39 papers on India, 65 papers on China, and 34 papers on all of Sub-Saharan Africa. This compares to 2383 papers on the U.S. over the same time period.

They then go on to show about 75 percent of the cross-country variation in the geographical focus of research is explained by GDP per capita and by population. Countries that have higher GDP are more likely to be the focus of research. This is a disappointing result if you are interested in developing countries (as the authors of the paper clearly are). Surprisingly though:

...the U.S. is not an outlier in the volume of research that is produced on it... the volume of research for the U.S. lies well within the predicted confidence interval and excluding the U.S. leads to the same coefficient estimates as its inclusion. In other words, a lot more is produced on the U.S. because it is rich and it is big; the natural comparator for the U.S. would be all of Europe and here, the volume of research is very similar.

However, when it comes to the elite journals, the U.S. is a clear outlier:

The difference between the U.S. and the rest of the world is substantial — 6.5% of all papers published on the U.S. are in the top tier journals relative to 1.8% of papers from other countries.

For comparative purposes, over their 20-year period there were 2,383 publications on the U.S. published in the top five economics journals, and one on New Zealand (yes, you read that right, just one - I don't know which article it was, sorry).

So, is this discrimination against non-U.S. research? Perhaps. Or, it could be as simple as the U.S. having a greater density of top-quality researchers, who are more likely to publish in top-quality journals, and who, because they are located in the U.S., have readier access to U.S. data. Or, perhaps the quality of U.S. data is higher. Das et al. point out that data quality has a superstar effect to it (similar to the superstar effects in the labour market that I have written about before):

Researchers converge on the “best” dataset even if it is 1% better than other data available, and the initial work creates a focal point for further research with the same data.

Again, like the paper I discussed yesterday, there isn't necessarily a causal interpretation to these results (doing research on the U.S. doesn't necessarily cause papers to be accepted into top journals). But it is disappointing, particularly given the quality of linked administrative data that we have in New Zealand through the Integrated Data Infrastructure, which (I think) should be attractive for publication in top journals.

Why sports stars are paid more than teachers

Last September, Don Boudreaux wrote this letter:

Ms. Montgomery:

I regret that you’re offended by my claim that “Only by thinking at the margin can we correctly understand why the wages of life-saving first-responders are lower than are the wages of NFL players and of Hollywood starlets and why this fact is a good thing for society.”  You allege that “Real people know it’s wrong and dangerous that men playing games get paid so much more than men and women who save lives and educate our children.”

I agree that most people are troubled that the likes of Tom Brady and Jennifer Lawrence earn far higher pay than does any firefighter or school teacher.  But this reality reflects not people’s correct understanding of a failing economy but people’s incorrect understanding of a successful economy.  It reflects also a failure of economists to better teach basic economics to the general public.  So let me ask: would you prefer to live in a world in which the number of people who can skillfully fight fires and teach children is large but the number of people who can skillfully play sports and act is very tiny, or in a world in which the number of people who can skillfully fight fires and teach children is very tiny but the number of people who can skillfully play sports and act is large?

Boudreaux's argument is that sports and movie stars are paid more than teachers or firefighters because there are fewer of them relative to the demand for their labour. In comparison, the higher labour supply of teachers and firefighters (compared with demand for them) leads to lower equilibrium wages. However, labour supply is only part of the story. The more important part is that the supply is low 'relative to the demand for their labour'. It isn't so much that there are few potential sports and movie stars (arguably there are many wannabe stars who are nearly as good), but that the demand for them is so high.

Why is demand for sports and movie stars so high? Because of 'superstar effects', which were first described by Sherwin Rosen in the 1980s (and which I have written about before). If a worker can satisfy the demand (for entertainment, in this case) from many consumers, they get paid a higher wage (a 'superstar' wage). Essentially, the worker is rewarded for generating very high revenues for their employer. Since movies or sports are watched (and paid for) by many consumers, this generates a lot of revenue for movie production companies and sports teams, who pass on some of these high revenues to their stars as higher wages. I'm sure that if teachers or firefighters could satisfy the demands of a much greater number of consumers (presumably students or victims of fire, respectively), then they could earn superstar wages as well.

Maybe in the future, teachers who teach on MOOCs (Massive Open Online Courses) will earn superstar wages? After all, "Massive" implies that they will be satisfying the demand from a lot of students. Of course, MOOCs would need to start making some money first (which is another topic I have written on before, see here and here).

[HT: Marginal Revolution, last September]

Footballer earnings, superstar and tournament effects

In ECON110, we talk about the reasons that not all workers in the same labour market receive the same wage. In the 1980s, Sherwin Rosen identified 'superstar effects', where if a worker can satisfy the demand from many consumers, they get paid a higher wage. Essentially, the worker is rewarded for generating high revenues for their employer, as you would expect. This explains much of the rise in salaries over time for top sportspeople - as television (and more recently internet) viewership has grown, the value generated by a top sportsperson (in terms of the number of viewers they attract) has grown, and their salaries have grown as a result.

Rosen, along with Ed Lazear, also described 'tournament effects'. With tournament effects, people are paid a 'prize' for their relative performance (that is, for winning the 'tournament'). The prize may take the form of a bonus, a raise, or a promotion. The point is that each worker only needs to be a little bit better than the second best worker in order to 'win' the tournament.

As an example of tournament effects, consider a team of 20 players, who are all roughly equally talented but can be ranked from 1st to 20th in terms of their ability to attract consumers to buy products that they have endorsed. Let's assume that each player can endorse no more than two products (due to time constraints). Now say that there are 20 firms willing to pay the players to endorse their products, and we can rank the companies by their willingness-to-pay. The firm with the highest willingness-to-pay is willing to pay $20 million, and the second $19 million, the third $18 million, and so on down to the last firm which is willing to pay just $1 million. What happens?

The top player receives endorsements of $39 million (from the two firms willing to pay the most), the second player receives $35 million (from the third and fourth firms), the third player receives $31 million (from the fifth and sixth firms), and so on. The table below shows player earnings for all 20 players (ranked from 1st to 20th). Notice that the top earning players earn a lot, but half of the players earn nothing. This arises even though the players are all roughly equally talented, and leads to a highly skewed earnings distribution.

Obviously the example above is totally made up and very simplistic, but it's not so far from what we observe in the real world. Consider the earnings of top footballers. Even if we consider only the top ten players (by earnings, according to Forbes' 2016 list), this is what we see:

It would be difficult to argue that Ronaldo or Messi generate more than twice as much value as the others on this list, so the difference must be generated by something other than just superstar effects; that is, tournament effects. That is, when we look at players at a similar level of play, the difference in earnings is mostly tournament effects. In contrast, the difference in average salaries in England between Premier League footballers (£1.7 million) and League Two footballers (£40,350) is likely to be a mix of superstar effects (Premier League footballers generate more value for their employers than League Two footballers) and tournament effects (there's a limited number of places for Premier League footballers, so slightly worse players end up in lower divisions paying less).

One last point: It's been argued (I saw this argument first in Tim Harford's book The Logic of Life) that the size of the 'prize' for a tournament will be larger the more luck is involved. That is, if the difference between the tournament 'winner' and the others is mostly luck, the size of the bonus for working hard to win the tournament must be high in order to sufficiently incentivise the worker to work hard. So, if you buy that the difference in the graph above is mostly a tournament effect, does that mean that the earnings difference between Ronaldo and Messi at the top, and Neymar in third, is mostly down to luck?