There’s an old joke that economics is too important to be left to economists. In the same vein, I think rich people are too important to be left to the self-help industry.
Yes, the popular appeal of you-can-get-rich-too books is obvious. But what’s not obvious is why so few social scientists study wealth.1 Clearly, the public thirsts for serious inquiries about the rich. (Thomas Piketty’s opus on inequality was a bestseller.) But for the most part, social scientists are content to focus on ‘poverty’ and let the self-help gurus wax about ‘wealth’.
The irony, in my view, is that poverty and wealth are two sides of the same coin. Concentrated wealth begets concentrated poverty. Still, there is an asymmetry between the two extremes. As a rule, poor people have little power, which means they cannot be blamed for their own poverty. But almost by definition, the rich wield power to their own benefit, which means they create the conditions of their own opulence … and everyone else’s misery.
Given their power over society, I find myself on a research kick studying rich people. (Earlier entries: a, b, c, and d.) This post concludes the binge with a look at what drives wealth concentration among the richest Americans. I find that there’s a straight line between wealth concentration, corporate consolidation, and the strategy of ‘buying, not building’. In short, Peter Thiel is correct when he says that ‘competition is for losers’.
A neoliberal experiment
Speaking of competition and losers, Ronald Reagan set the tone of the neoliberal era when, in 1981, he fired 11,000 striking air-traffic controllers. The message? Workers were losers who would be subjected to the discipline of competition. Reagan called it ‘morning in America’. But really, it was ‘morning for American big business’.
Today, we are well into the next-day’s hangover, and we know how the party played out. For workers, it was a disaster. But for the rich, it was an incredible boon. Wealth didn’t trickle down so much as it got catapulted up. The result, as Figure 1A shows, was a relentless rise in the concentration of American wealth.
Interestingly, as wealth got catapulted from the poor to the rich, it also got transported from the mega rich to the supremely rich. This is the story told by Figure 1B. Here, I’ve focused on the richest Americans — the folks who grace the Forbes 400 list. Even here, among the upper crust of elites, wealth has grown more concentrated. Why?
As you’ll see, the culprit seems to be the stock market. But before we interrogate our suspect, let’s have a quick look at the brethren of the American rich — the globetrotting, jet-fuel belching species otherwise known as Earth’s billionaires.
A billionaire hammer
They say that when you’ve got a hammer, everything looks like a nail. Well, lately my hammer has been data from Forbes. Which means that I can’t seem to write a post without pounding on the world’s billionaires. (Luckily, they deserve it.)
Backing up a bit, the reason I’m holding a Forbes hammer is that since late 2021, I’ve been scraping Forbes’ global billionaire data. The endeavor started with an email from my colleague DT Cochrane, who pointed out the value of having a daily snapshot of billionaires’ wealth. I concurred, and set some billionaire-scraping code in motion. The result is that today, I have just over two years worth of daily data about the wealth of the world’s billionaires.
Billionaires.
The word itself evokes a kind of class coherence. But the reality is that billionaires are a deceptively unequal group. For example, the world’s billionaires have a median wealth of about $2.4 billion. And to most people, that seems like a tremendous fortune. But compared to the $240B wealth of the world’s richest man, Elon Musk, $2.4B is chump change. Heck, Musk spent 16 times more than that just to buy a social-media company and set it in fire.
The message is that billionaire wealth is both spectacularly large and spectacularly concentrated. And as it turns out, this concentration varies with a coherent pattern. Figure 2 shows the picture over the last two years. Something is driving billionaire wealth concentration up and down. What could it be?
The stock market confesses
The physicist Richard Feynman claimed to dislike reading scientific papers because, as his biographer James Gleick put it, “every arriving paper was like a detective novel with the last chapter printed first.”2 The format, Feynman complained, spoiled the fun of doing detective work.
With apologies to detectives like Feynman, I’m about to spoil the fun. When it comes to wealth concentration among billionaires, the main driver appears to be the stock market.
To be fair, the culprit was fairly obvious. Almost without exception, the richest individuals have their fortunes invested in corporate property rights — rights which are traded on the stock market.3 So if we want to understand inequality in these investments, the stock market is the primary suspect. Still, you might be surprised by the detail of its testimony.
In Figure 3, I bring the stock market in for questioning. ‘What drives billionaire wealth concentration?’ I ask. The stock market squeals, ‘I do! I do!’
A longer track record
Looking at the confession in Figure 3, the detective in me worries that it’s too good to be true. Seriously, the fit between the S&P 500 and billionaire wealth concentration is so tight that it makes me fret that I’ve flubbed the analysis. Fortunately, our suspect has given other confessions.
Turning to the United States, we find a similar connection between elite wealth concentration and the movement of the stock market. Figure 4 shows the record. The blue curve plots the level of wealth concentration among the Forbes 400. The red curve plots the rise of the S&P 500, measured relative to US GDP per capita. Again, it’s a compelling testimony. Elite wealth concentration seems to be driven by the stock market.
Within the confession, a (math) puzzle
It this point, it’s tempting to close the case. When questioned about elite wealth concentration, the stock market confessed to the crime. And yet, if we think more deeply about the testimony, we find that it comes with a puzzle.
The mystery starts when we realize that the stock market is not one thing. It is many things — many corporate stocks that each have a mind of their own. Now, when we look at the S&P 500, we’re measuring the average movement of these stocks. Fine. But the thing about averages is that they typically tell us nothing about measures of spread. Yet elite wealth concentration is definitely a measure of spread.
And so we have a mathematical puzzle. The stock-market average seems to ‘know’ about something that it shouldn’t. Why?
Growth through inequality
To unwrap our stock-market puzzle, we need to review some math. In general, measures of spread are unrelated to measures of central tendency.4 There is, however, an exception. It happens when growth is driven by inequality.
To illustrate this exception, we’ll turn to a simple thought experiment. Imagine two people, Alice and Bob, who both have $1 in their pocket. Over time, we hand out money to the pair, thereby increasing their pool of wealth. But the catch is that we give the money exclusively to Bob.
Table 1 shows how these handouts affect Alice and Bob’s average wealth, along with their wealth concentration. As we hand money to Bob, Alice and Bob’s average wealth grows. But this average is driven not by shared prosperity, but by rising inequality. Importantly, in this situation of one-sided handouts, the wealth average becomes an (unwitting) indicator of the level of wealth spread.
Table 1: Growth through inequality
| Year | Alice’s wealth | Bob’s wealth | Average wealth | Wealth concentration (Gini index) |
|---|---|---|---|---|
| 1 | $1 | $1 | $1 | 0.00 |
| 2 | $1 | $3 | $2 | 0.50 |
| 3 | $1 | $9 | $5 | 0.80 |
Note: To measure wealth concentration, I’ve used the sample-size adjusted Gini index. For details, see this paper by George Deltas.
Putting on our detective hats, it seems likely that similar behavior — what I’m calling ‘growth through inequality’ — explains our stock-market results. We’ve found that the S&P 500 index (an average) is connected to levels of elite wealth concentration (a form of spread). But this connection only makes sense if the S&P 500 is an (unwitting) indicator of stock-market inequality.
So with inequality in mind, we need to peer inside the S&P 500 to see how it gets made.
Inside the S&P 500
I realize that studying the plumbing of a stock index makes for less-than-captivating reading. So let me cut to the chase: in simple terms, the S&P 500 tracks the total market capitalization of the 500 largest US firms.
For the math averse, you can take this fact and skip to Figure 5. But for the equation lovers, here are the details.
The S&P 500 tracks the average stock price of five hundred of the largest US companies.5 Importantly, S&P weights the average according to each company’s size, measured in terms of outstanding shares.
Here’s the math. Let P_i be the stock price of company i . And let Q_i be the number of outstanding shares in this company. Summing over all 500 companies, the S&P 500 is then:
Importantly, when we multiply stock price P by the number of shares Q , we are calculating a company’s market capitalization, K . So in simplified terms, the S&P 500 sums the market capitalization of the 500 largest US firms:
Backtracking slightly, note that I’ve used the ‘ \propto ’ symbol (which stands for ‘proportional to’) in the formulas above. I’ve used it because I’m excluding some adjustments that go into calculating the actual S&P 500 index. Since these adjustments don’t affect my argument, I’m going to ignore them.6
Forging ahead, our equations indicate that the S&P 500 is proportional to the total market capitalization of the 500 largest US companies. On that front, the empirical evidence suggests the same thing, as shown in Figure 5.7
The reason I’m bothering with this stock-index math is that I want to look at the components of the S&P 500. We now understand that these components are basically the market capitalization of the 500 largest US firms. Let’s use this knowledge to peer inside the S&P sausage.
Figure 6 shows a different view of the S&P 500. Rather than summing the market capitalization of our top 500 firms, I’ve plotted the market-cap values for each firm. Then I’ve connected the values with a pretty rainbow that shows the evolving composition of the S&P 500 index. Besides being nice eye candy, this market-cap rainbow (presumably) holds the key to understanding why the S&P 500 relates to elite wealth concentration.
Growth through corporate concentration
Having dissected the S&P 500, we’re ready to return to our original question: why does a stock-market average tells us about a measure of elite wealth spread? The answer, it turns out, is that what appears as stock-market ‘growth’ is in part, an artifact of rising stock-market concentration.
Here’s how it works. Returning to our Alice-and-Bob thought experiment, we were able to increase Alice and Bob’s average wealth by handing money solely to Bob. But this rising average didn’t indicate shared prosperity. It was an artifact of the rich getting richer.
Turning to the stock market, the situation is similar. Except that Alice and Bob are not people, they are firms. The Bob-like firms are giant companies like Apple, Microsoft, Google and Amazon — four corporations that have a combined market capitalization of about $5.9 trillion. The Alice-like firms are the smaller companies on the S&P 500.
What’s important is that collectively, our four Bob-like firms account for about a sixth of the value of the entire S&P 500. So if their stock rises, it will buoy the whole S&P 500 index. But this buoyancy isn’t really ‘growth’; it’s an artifact of corporate concentration — rich firms getting richer.
In more general terms, when we look at the rise of the S&P 500 index, we find that it is connected to levels of corporate concentration. Figure 7 makes the case. In Figure 7A, I’ve plotted a measure of corporate concentration — the Gini index of market capitalization among the 500 largest US firms. When this Gini index grows, it signals that corporate wealth is being concentrated in the hands of the richest firms. Looking at Figure 7B, we see that this corporate concentration is tied to the movement of the S&P 500 (measured relative to US GDP per capita).
So in Figure 7, we’ve got evidence that the S&P 500 is an unwitting indicator of US corporate concentration. And it’s not because S&P analysts tried to make that happen. (They didn’t.) It’s because historically, an important part of (apparent) stock-market growth is simply the richest firms getting richer.
To the owners go the spoils
So what happens as rich firms get richer? Well, the rich owners of these firms also get richer.
Today, for example, the richest firms are companies like Amazon, Google and Microsoft. Unsurprisingly, the individuals who own these firms — Jeff Bezos, Larry Page, Bill Gates and Sergey Brin — are consistently among the world’s richest people. Bringing dynamics into the fold, as these big-tech companies consolidate their holdings, we expect that this consolidation will concentrate wealth in the hands of big-tech owners. In other words, the concentration of corporate wealth should beget the concentration of individual wealth.
So does it? At least in the United States, the answers seems to be yes. Figure 8 makes the case. Looking at the richest firms and the richest individuals, we find that the concentration of corporate wealth (horizontal axis) strongly predicts the concentration of individual wealth (vertical axis). To the richest owners go the spoils of oligopoly.
Concentration through acquisition
At this point we’ve got some fairly incendiary evidence. The ‘crime’ of elite wealth concentration seems to be tied directly to corporate oligarchy. But before we put the case to rest, let’s consider the testimony of the defense’s expert witnesses. I’m talking, of course, about neoclassical economists.
Ostensibly, neoclassical economists love competitive markets and hate monopoly. But beginning in the 1980s, a weird thing happened; economists at the University of Chicago started to argue that despite lacking competition, monopolies could still be ‘efficient’. Their reasoning was that if monopolists actually behaved badly, they would be undercut by competitors, and their monopoly would be undone. Therefore, if a monopoly exists, it must be because the monopolist is doing what the market wants.
Now the logic here is torturous. We’re positing imaginary competition to justify a lack of real-world competition. But then again, neoclassical economists have never let the real world get in the way of their imaginations. And in this case, the goal of the imaginary theorizing was always obvious: it was designed get government out of the way and allow big corporations to purchase their way to power.
Backing up a bit, politicians are rarely incensed when a big corporation builds more factories. So in that sense, the government is not opposed to big companies getting bigger. But from a corporate vantage point, factory building is a less-than-ideal route to bigness. The problem is simple: if everyone builds more factories, it leads to ‘free run of production’ (Thorstein Veblen’s term) which then collapses profits. So savvy corporations are always looking for a better route to power. And that better route is to buy instead of build.
The buy-not-build tactic is hardly rocket science. As Jonathan Nitzan and Shimshon Bichler observe, when you buy your competitor, you solve two problems at once: you accumulate power and reduce your competition. The difficulty, though, is that this buy-not-build tactic has the appearance of being a blatant power grab. So there’s the risk that an entrepreneurial government might get in the way.
That’s where Chicago-school theorists come in. Starting in the 1980s, they successfully preached an ideology that got the government out of the way. The net result is the modern corporate landscape, forged in large part by a string of government-approved corporate acquisitions.
Tech monopolist Google has been a prime benefactor of this buy-not-build tactic. As Cory Doctorow notes, “Google didn’t invent its way to glory — it bought its way there.” He continues:
Google’s success stories (its ad-tech stack, its mobile platform, its collaborative office suite, its server-management tech, its video platform …) are all acquisitions.
The same strategy holds for most of today’s corporate oligarchies. Their tentacles have largely been bought, not built. On this front, the numbers don’t lie: the consolidated corporate landscape of the 21st century was forged by a massive, neoliberal wave of mergers and acquisitions
Let’s have a look at the tsunami.
To quantify the scale of mergers and acquisitions, we’ll turn to an index called the buy-to-build ratio. As the name suggests, the buy-to-build ratio measures the corporate proclivity for buying other companies instead of building new capacity. Created by Jonathan Nitzan and Shimshon Bichler (and first published in 2001), the buy-to-build ratio takes the value of corporate mergers and acquisitions and divides them by the value of greenfield investments. The greater this buy-to-build ratio, the more that corporations are buying (and not building) their way to power.
As I’ve alluded, the neoliberal era saw a massive wave of corporate mergers and acquisitions. As a result, from 1980 to 2000, the US buy-to-build ratio jumped nearly tenfold. And guess what accompanied this acquisition wave. That’s right … a sharp rise in corporate concentration.
Figure 9 shows the connection. As the US buy-to-build ratio increased (horizontal axis), so did the market-cap concentration among the largest US firms (vertical axis). The lesson is clear: over the last forty years, big corporations have been buying their way to consolidated power.
Competition is for losers
One of the (few) nice things about living in an era of concentrated corporate power is that modern plutocrats are brash enough to speak plainly about their ambitions. Forget the arcane language wielded by Chicago-school economists. Today’s plutes — men like Peter Thiel — say the quiet part out loud. If you want to ‘capture lasting value’, Thiel proclaims, ‘look to build a monopoly’. Or in mantra form, ‘competition is for losers’.
John D. Rockefeller would be proud.
Speaking of Rockefeller, did you know that he was one of the principle funders of the University of Chicago? Ironic, isn’t it. Rockefeller, like Thiel, spoke openly about his pursuit of power and personal enrichment. So if, during Rockefeller’s life, someone had connected elite wealth concentration to corporate consolidation, the reaction would have been “Well, that’s obvious.”
Fast forward to the 1980s and the connection became not-so obvious, at least to economists. And that’s thanks in large part to Rockefeller’s Chicago-school investment, which pumped out decades worth of pro-oligarch propaganda.
Today, we’ve come full circle. Billionaires like Peter Thiel are so hubristic that they speak brazenly about their pursuit of power, laying bare their inner robber baron. The upshot to this plute bravado is that few people will be surprised by the straight line that connects corporate oligarchy with the concentration of elite wealth.
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Sources and Methods
US distribution of wealth
In Figure 1, I calculated the US wealth Gini index using data from the World Inequality Database. Income threshold data is from series thwealj992. Income share data is from series shwealj992.
Forbes data
I scraped historic Forbes 400 data from many corners of the internet. You can find notes about the specific sources here.
Data for global billionaire wealth is from the Forbes real-time billionaire list. I’ve been keeping a daily archive of the list since October 2021.
S&P 500
Data for the S&P 500 is from two sources. For Figure 3, I downloaded the daily data using the R package tidyquant, series ^GSPC. The long-term S&P 500 data plotted in Figure 4 is from Robert Shiller, available here.
US nominal GDP per capita
Data for US nominal GDP is from:
- 1983–2021: Bureau of Economic Analysis, Table 1.1.5
- 2021–2023: quarterly GDP per capita data from FRED, series A939RC0Q052SBEA.
Data for US population is from:
- 1983–2021: World Bank, series SP.POP.TOTL
Market capitalization
Data for the market cap of the largest US companies (Figure 5) is from Compustat. To calculate each company’s market cap, I took the number of shares outstanding (series csho) and multiplied it by the annual closing share price (series prcc_c).
Buy-to-build ratio
The buy-to-build ratio is calculated by taking the value of corporate mergers and acquisitions and dividing it by the value of gross fixed capital formation (which is a rough measurement of ‘greenfield’ investment).
Compiling the requisite historical data for this calculation is no small task. The main hurdle, as Jonathan Nitzan notes, is that “there are no systematic historical time series for mergers and acquisitions”. So any estimate must piece together a hodgepodge of different sources.
In this post, I’ve used Joseph Francis’ 2013 estimates for the US buy-to-build ratio. You can download his data here, and read his methods here. It’s also worth reading Bichler and Nitzan’s comments on Francis’ calculation, which are available here.
Speaking of wealth and poverty
Still reading? Here’s a little reward for getting to the end of the article — a piece of research that I couldn’t fit in the main text. It turns out that social scientists (at least those who write in English) haven’t always prioritized studying ‘poverty’ over ‘wealth’. Figure 10 makes the case using data from the Google English corpus.
Two centuries ago, the phrase ‘cause of wealth’ was just as popular as the phrase ‘cause of poverty’. And that makes sense. In 1776, Adam Smith published his famous tome about the wealth of nations. Clearly, he and other political economists wanted to understand wealth. But throughout the 19th century, interest in wealth waned, leading to today’s dichotomy. Judging by word count, about ten times as many people study the ‘cause of poverty’ as study the ‘cause of wealth’.
Notes
Further reading
Bichler, S., & Nitzan, J. (2013). Francis’ buy-to-build estimates for Britain and the United States: A comment. Review of Capital as Power, 1(1), 73–78. https://capitalaspower.com/2013/02/francis-buy-to-build-estimates-for-britain-and-the-united-states-a-comment/
Francis, J. (2013). The buy-to-build indicator: New estimates for Britain and the United States. Review of Capital as Power, 1(1), 63–72. https://capitalaspower.com/2013/03/the-buy-to-build-indicator-new-estimates-for-britain-and-the-united-states/
Nitzan, J. (2001). Regimes of differential accumulation: Mergers, stagflation and the logic of globalization. Review of International Political Economy, 8(2), 226–274. https://bnarchives.yorku.ca/3/