Tag Archives: Finance

Investors Not Barking

Detective: “Is there any other point to which you would wish to draw my attention?”

Holmes: “To the curious incident of the dog in the night-time.”

Detective: “The dog did nothing in the night-time.”

Holmes: “That was the curious incident.”

We’ve seen several centuries of continuing economic growth enabled by improving tech (broadly conceived). Some of that tech can be seen as “automation” where machines displace humans on valued tasks.

The economy has consistently found new tasks for humans, to make up for displaced tasks. But while the rate of overall economic growth has be relatively steady, we have seen fluctuations in the degree of automation displacement in any given industry and region. This has often led to local anxiety about whether we are seeing the start of a big trend deviation – are machines about to suddenly take over most human jobs fast?

Of course so far such fears have not yet been realized. But around the year 2000, near the peak of the dotcom tech boom, we arguably did see substantial evidence of investors suspecting a big trend-deviating disruption. During a big burst of computer-assisted task displacement, the tech sector should soon see a big increase in revenue. So anticipating a substantial chance of such a burst justifies bigger stock values for related firms. And this graph of the sector breakdown of the S&P500 over the last few decades shows that investors then put their money where their mouths were regarding such a possible big burst:


In the last few years, we’ve heard another burst of anxiety about an upcoming big burst of automation displacing humans on tasks. It is one of our anxieties du jour. But if you look at the right side of the graph above you’ll note that are not now seeing a boom in the relative value of tech sector stocks.

We see the same signal if we look at majors chosen by college graduates. A big burst of automation not only justifies bigger tech stock values, it also justifies more students majoring in tech. And during the dotcom boom we did see a big increase in students choosing to major in computer science. But we have not seen such an increase during the last decade.

So the actions of both stock investors and college students suggest that they do not believe we are at substantial risk of a big burst of automation soon. These dogs are not barking. Even if robots taking jobs is what lots of talking heads are talking about. Because talking heads aren’t putting their money, or their time, where their mouths are.

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What Does Harvard Do Right?

Is Harvard the top rated college because it is the most clever in deciding who to admit? Not obviously. Instead, in the short run Harvard can gain plenty from a positive feedback loop: the best people apply and prefer to go there, which adds a glow to those who graduate from there, which makes the best want to apply, and so on.

While this seems an obvious and simple story, I must admit I haven’t been thinking enough in such terms, probably in part because I haven’t seen formal economic models that capture this story well. I thank venture capital (VC) titan Marc Andreessen for clarifying. Here is part of a 14 May twitter chat between him (MA) and myself (RH):

RH: VC is dominated by a few firms. What is the scale economy? Few geniuses? Info of seeing most pitches? Ability to create new fashions? Other?

MA: Core dynamic: A few firms have positive selection on their side; the other firms have adverse selection working against them.

The battle among VC firms is less “who is smarter?” than “who do the best founders approach first?”.

RH: OK, but why approach the top few first? What is more attractive about being funded by them vs others?

MA: Founders care about the VC brand halo because potential employees, potential customers, and other potential investors care.

RH: Is it just that top VC get first pick, so they are better picks, so their picks get halo by being in that pool, rinse & repeat?

MA: Yes, that’s the core positive feedback loop. How it starts is less meaningful than how it perpetuates.

Core dynamic: A few firms have positive selection on their side; the other firms have adverse selection working against them.

The battle among VC firms is less “who is smarter?” than “who do the best founders approach first?”.

The main historical driver of positive selection is prior success: a halo branding effect that new startups seek.

In essence, a new startup uses its VC’s brand as a credibility bridge until the startup establishes its own brand.

RH: Sure, but the question is why some VC brands shine brighter. Their money isn’t any more green.

MA: They have an aura of success as a consequence of having previously funded successful startups.

Arguably these dynamics are changing in real time in some interesting ways:

RH: Is there a prediction on if VC industry will become more or less concentrated as result of these changes?

MA: My belief is that VC is restructuring the same way retail stores, law firms, accounting firms, and investment banking did:

This seems to be the hallmark of a professionalizing industry being run properly. You either go big or you go specialist.

RH: I guess the key idea is that there are big scale economies with doing standard tasks, but big diseconomies for specialized tasks.

MA: Yes, but with the subtlety that the well-run scale players are also excellent at many of the specialized tasks.

RH: Many, but not most, or the specialized shops couldn’t exist long.

MA: This is exactly what happened in the talent agency business in the 1980s and 1990s. The big agencies got great at many things.

The specialized shops have to stay small and stay laser-focused on particular areas of specialized advanced competency.

But of course similarly, a scaled franchise firm that gets sloppy runs the same risk, can degrade itself into the middle tier.

RH: Summary: long trend is to scale given tasks, but also task specialization. Overall scale rises, but falls locally when specialize.

MA: Right, exactly. And this explains the size distribution — the scaled players have to be big; the boutiques have to stay small.

You see this in investment banking. You either work with Goldman Sachs or you work with a small boutique specialist bank.

RH: This makes sense, but I’m not sure we have any formal models that predict this correlation nicely.

This same sort of story also seems to work in the short run to explain why some journals have higher prestige. It is not so much that top journal editors are more clever, or use a smarter system to review submissions. It is just that the best papers are submitted there first, which makes the average quality of their publications higher, and so on.

In the long run, we see changes in the prestige rankings of these colleges, journals, investment banks, and venture capital funds. The key question is: what determines those long run changes? Do competitors with slightly better ways to evaluate or help submissions slowly win out over others? Or do other factors dominate?

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Financial Status

At a finance conference last year, I learned this: Instead of saving money directly for their own retirement, many workers have their employers save for them. Those employers hire in-house specialists to pick which specialty consulting firms to hire. These consulting firms advise employers on which investment firms to use. And those investment firms pick actual productive enterprises in which to invest. All three of these intermediaries, i.e., employer, consultant, and investor, take a cut for their active management.

Even employees who invest for themselves tend to pick at least one high fee intermediary: an active-management investment firm. Few take the low cost option of just directly investing in a low-overhead index fund, as recommended by academics for a half-century.

I’ve given talks at many active-management investment firms over the years. They pay speakers very well. I’ve noticed that (like management consults) they tend to hire very visibly impressive people. They also give big investors a lot of personal quality time, to create personal relationships. Their top people seem better at making investors like them than at picking investments. One math-focused firm said it didn’t want more investors because investors all demand more face time and influence over investment choices.

Since 1880 the fraction of US GDP paid for financial intermediation has gone from 2% to 8%. And:

The unit cost [relative to asset income] of financial intermediation appears to be as high today as it was around 1900. This is puzzling. Advances in information technology (IT) should lower the physical transaction costs of buying, pooling and holding financial assets. Trading costs have indeed decreased, but trading volumes have increased even more, and active fund management is expensive. … Investors spend 0.67% of asset value trying (in vain on average, by definition) to beat the market. … While mutual funds fees have dropped, high fee alternative asset managers have gained market share. The end result is that asset management unit costs have remained roughly constant. The comparison with retail and wholesale trade is instructive. In these sectors … larger IT investment coincides with lower prices and lower (nominal) GDP shares. In finance, however, exactly the opposite happens. … A potential explanation is oligopolistic competition but … the historical evidence does not seem to support the naive market power explanation, however. (more)

Our standard academic story on finance is that it buys risk-reduction, and perhaps also that we are overconfident in finance judgements. But it isn’t clear we’ve had much net risk reduction, especially to explain a four times spending increase. (In fact, some argue plausibly that those who take more risk don’t actually get higher returns.) On overconfidence, why would it induce such indirection, and why would its effects increase by such a huge factor over time?

Finance seems to me to be another area, like medicine, schools, and many others, where our usual standard stories just don’t work very well at explaining the details. In such cases most economists just gullibly plow ahead trying to force-fit the standard story onto available data, instead of considering substantially different hypotheses. Me, I try to collect as many pieces of related puzzling data as I can, and then ask what simple but different stories might account at once for many of those puzzles.

To me an obvious explanation to consider here is that we like to buy special connections to prestigious advisors. We look good when bonded to others who look good, and we treat investor relations as especially important bonds. We seem to get blamed less for failures via prestigious associates, and yet are credited for most of our success via them. Finally, we just seem to directly like prestigious associations, even when others don’t know of them. And we may also gain from associating with others who share our advisors.

To explain the change in finance over time, I’ll try my usual go-to explanation for long-term changes in the last few centuries: increasing wealth. In particular, social bonds as a luxury that we buy more of when richer. This can explain the big increases we’ve seen in leisure, product variety, medicine, and schooling.

So as we get rich, we spend larger fractions of our time socializing, we pay more for products with identities that can tie us to particular others, we spend more to assure associates that we care their health, and we spend more to visibly connect with prestigious associates. Some of those prestigious associates are at the schools we attend, the places we live, and via the products we buy. Others come via our financial intermediaries.

This hypothesis suggests an ironic reversal: While we usually play up how much we care about associates, and play down our monetary motives, in finance we pretend to make finance choices purely to get money, while in fact we lose money to gain prestigious associates.

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Light On Dark Matter

I posted recently on the question of what makes up the “dark matter” intangible assets that today are most of firm assets. Someone pointed me to a 2009 paper of answers:


[C.I. = ] Computerized information is largely composed of the NIPA series for business investment in computer software. …

[Scientific R&D] is designed to capture innovative activity built on a scientific base of knowledge. … Non-scientific R&D includes the revenues of the non-scientific commercial R&D industry … the costs of developing new motion picture films and other forms of entertainment, investments in new designs, and a crude estimate of the spending for new product development by financial services and insurance firms. …

[Brand equity] includes spending on strategic planning, spending on redesigning or reconfiguring existing products in existing markets, investments to retain or gain market share, and investments in brand names. Expenditures for advertising are a large part of the investments in brand equity, but … we estimated that only about 60 percent of total advertising expenditures were for ads that had long-lasting effects. …

Investment in firm-specific human and structural resources … includes the costs of employer-provided worker training and an estimate of management time devoted to enhancing the productivity of the firm. … business investments in firm-specific human and structural resources through strategic planning, adaptation, reorganization, and employee-skill building. (more; HT Brandon Pizzola)

According to this paper, more firm-specific resources is the biggest story, but more product development is also important. More software is third in importance.

Added 15Apr: On reflection, this seems to suggest that the main story is our vast increase in product variety. That explains the huge increase in investments in product development and firm-specific resources, relative to more generic development and resources.

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Firms Now 5/6 Dark Matter!

Scott Sumner:

We all know that the capital-intensive businesses of yesteryear like GM and US steel are an increasingly small share of the US economy. But until I saw this post by Justin Fox I had no idea how dramatic the transformation had been since 1975:


Wow. I had no idea as well. As someone who teaches graduate industrial organization, I can tell you this is HUGE. And I’ve been pondering it for the week since Scott posted the above.

Let me restate the key fact. The S&P 500 are five hundred big public firms listed on US exchanges. Imagine that you wanted to create a new firm to compete with one of these big established firms. So you wanted to duplicate that firm’s products, employees, buildings, machines, land, trucks, etc. You’d hire away some key employees and copy their business process, at least as much as you could see and were legally allowed to copy.

Forty years ago the cost to copy such a firm was about 5/6 of the total stock price of that firm. So 1/6 of that stock price represented the value of things you couldn’t easily copy, like patents, customer goodwill, employee goodwill, regulator favoritism, and hard to see features of company methods and culture. Today it costs only 1/6 of the stock price to copy all a firm’s visible items and features that you can legally copy. So today the other 5/6 of the stock price represents the value of all those things you can’t copy.

So in forty years we’ve gone from a world where it was easy to see most of what made the biggest public firms valuable, to a world where most of that value is invisible. From 1/6 dark matter to 5/6 dark matter. What can possibly have changed so much in less than four decades? Some possibilities:

Error – Anytime you focus on the most surprising number you’ve seen in a long time, you gotta wonder if you’ve selected for an error. Maybe they’ve really screwed up this calculation.

Selection – Maybe big firms used to own factories, trucks etc., but now they hire smaller and foreign firms that own those things. So if we looked at all the firms we’d see a much smaller change in intangibles. One check: over half of Wilshire 5000 firm value is also intangible.

Methods – Maybe firms previously used simple generic methods that were easy for outsiders to copy, but today firms are full of specialized methods and culture that outsiders can’t copy because insiders don’t even see or understand them very well. Maybe, but forty years ago firm methods sure seemed plenty varied and complex.

Innovation – Maybe firms are today far more innovative, with products and services that embody more special local insights, and that change faster, preventing others from profiting by copying. But this should increase growth rates, which we don’t see. And product cycles don’t seem to be faster. Total US R&D spending hasn’t changed much as a GDP fraction, though private spending is up by less than a factor of two, and public spending is down.

Patents – Maybe innovation isn’t up, but patent law now favors patent holders more, helping incumbents to better keep out competitors. Patents granted per year in US have risen from 77K in 1975 to 326K in 2014. But Patent law isn’t obviously so much more favorable. Some even say it has weakened a lot in the last fifteen years.

Regulation – Maybe regulation favoring incumbents is far stronger today. But 1975 wasn’t exact a low regulation nirvana. Could regulation really have changed so much?

Employees – Maybe employees used to jump easily from firm to firm, but are now stuck at firms because of health benefits, etc. So firms gain from being able to pay stuck employees due to less competition for them. But in fact average and median employee tenure is down since 1975.

Advertising – Maybe more ads have created more customer loyalty. But ad spending hasn’t changed much as fraction of GDP. Could ads really be that much more effective? And if they were, wouldn’t firms be spending more on them?

Brands – Maybe when we are richer we care more about the identity that products project, and so are willing to pay more for brands with favorable images. And maybe it takes a long time to make a new favorable brand image. But does it really take that long? And brand loyalty seems to actually be down.

Monopoly – Maybe product variety has increased so much that firm products are worse substitutes, giving firms more market power. But I’m not aware that any standard measures of market concentration (such as HHI) have increased a lot over this period.

Alas, I don’t see a clear answer here. The effect that we are trying to explain is so big that we’ll need a huge cause to drive it. Yes it might have several causes, but each will then have to be big. So something really big is going on. And whatever it is, it is big enough to drive many other trends that people have been puzzling over.

Added 5p: This graph gives the figure for every year from ’73 to ’07.

Added 8p: This post shows debt/equity of S&P500 firms increasing from ~28% to ~42% from ’75 to ’15 . This can explain only a small part of the increase in intangible assets. Adding debt to tangibles in the numerator and denominator gives intangibles going from 13% in ’75 to 59% in ’15.

Added 8a 6Apr: Tyler Cowen emphasizes that accountants underestimate the market value of ordinary capital like equipment, but he neither gives (nor points to) an estimate of the typical size of that effect.

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Irreducible Detail

Our best theories vary in generality. Some theories are very general, but most are more context specific. Putting all of our best theories together usually doesn’t let us make exact predictions on most variables of interest. We often express this fact formally in our models via “noise,” which represents other factors that we can’t yet predict.

For each of our theories there was a point in time when we didn’t have it yet. Thus we expect to continue to learn more theories, which will let us make more precise predictions. And so it might seem like we can’t constrain our eventual power of prediction; maybe we will have powerful enough theories to predict everything exactly.

But that doesn’t seem right either. Our best theories in many areas tell us about fundamental limits on our prediction abilities, and thus limits on how powerful future simple general theories could be. For example:

  • Thermodynamics – We can predict some gross features of future physical states, but the entropy of a system sets a very high (negentropy) cost to learn precise info about the state of that system. If thermodynamics is right, there will never be a general theory to let one predict future states more cheaply than this.
  • Finance – Finance theory has identified many relevant parameters to predict the overall distribution of future assets returns. However, finance theory strongly suggests that it is usually very hard to predict details of the specific future returns of specific assets. The ability to do so would be worth such a huge amount that there just can’t be many who often have such an ability. The cost to gain such an ability must usually be more than the gains from trading it.
  • Cryptography – A well devised code looks random to an untrained eye. As there are a great many possible codes, and a great many ways to find weaknesses in them, it doesn’t seem like there could be any general way to break all codes. Instead code breaking is a matter of knowing lots of specific things about codes and ways they might be broken. People use codes when they expect the cost of breaking them to be prohibitive, and such expectations are usually right.
  • Innovation – Economic theory can predict many features of economies, and of how economies change and grow. And innovation contributes greatly to growth. But economists also strongly expect that the details of particular future innovations cannot be predicted except at a prohibitive cost. Since knowing of innovations ahead of time can often be used for great private profit, and would speed up the introduction of those innovations, it seems that no cheap-to-apply simple general theories can exist which predict the details of most innovations well ahead of time.
  • Ecosystems – We understand some ways in which parameters of ecosystems correlate with their environments. Most of these make sense in terms of general theories of natural selection and genetics. However, most ecologists strongly suspect that the vast majority of the details of particular ecosystems and the species that inhabit them are not easily predictable by simple general theories. Evolution says that many details will be well matched to other details, but to predict them you must know much about the other details to which they match.

In thermodynamics, finance, cryptography, innovations, and ecosystems, we have learned that while there are many useful generalities, the universe is also chock full of important irreducible incompressible detail. As this is true at many levels of abstraction, I would add this entry to the above list:

  • Intelligence – General theories tell us what intelligence means, and how it can generalize across tasks and contexts. But most everything we’ve learned about intelligence suggests that the key to smarts is having many not-fully-general tools. Human brains are smart mainly by containing many powerful not-fully-general modules, and using many modules to do each task. These modules would not work well in all possible universes, but they often do in ours. Ordinary software also gets smart by containing many powerful modules. While the architecture that organizes those modules can make some difference, that difference is mostly small compared to having more better modules. In a world of competing software firms, most ways to improve modules or find new ones cost more than the profits they’d induce.

If most value in intelligence comes from the accumulation of many expensive parts, there may well be no powerful general theories to be discovered to revolutionize future AI, and give an overwhelming advantage to the first project to discover them. Which is the main reason that I’m skeptical about AI foom, the scenario where an initially small project quickly grows to take over the world.

Added 7p: Peter McCluskey has thoughtful commentary here.

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Big Signals

Between $6 and $9 trillion dollars—about 8% of annual world-wide economic production—is currently being spent on projects that individually cost more than $1 billion. These mega-projects (including everything from buildings to transportation systems to digital infrastructure) represent the biggest investment boom in human history, and a lot of that money will be wasted. …

Over the course of the last fifteen years, [Flyvbjerg] has looked at hundreds of mega-projects, and he found that projects costing more than $1 billion almost always face massive cost overruns. Nine out of ten projects faces a cost overrun, with costs 50% higher than expected in real terms not unusual. …

In fact, the number of mega-projects completed successfully—on time, on budget, and with the promised benefits—is actually too small for Flyvbjerg to determine why they succeeded with any statistical validity. He estimates that only one in a thousand mega-projects fit that criteria. (more; paper)

You can probably throw most big firm mergers into this big inefficient project pot.

There’s a simple signaling explanation here. We like to do big things, as they make us seem big. We don’t want to be obvious about this motive, so we pretend to have financial calculations to justify them. But we are purposely sloppy about those calculations, so that we can justify the big projects we want.

It would be possible to make prediction markets that accurately told us on average that these financial calculations are systematically wrong. That could enable us to reject big projects that can’t be justified by reasonable calculations. But the people initiating these projects don’t want that, so it would have to be outsiders who set up these whistleblowing prediction markets. But alas as with most whistleblowers, the supply of these sort of whistleblowers is quite limited.

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Multiplier Isn’t Reason Not To Wait

On the issue of whether to help now vs. later, many reasonable arguments have been collected on both sides. For example, positive interest rates argue for helping later, while declining need due to rising wealth argues for helping now. But I keep hearing one kind of argument I think is unreasonable, that doing stuff has good side effects:

Donating to organizations (especially those that focus on influencing people) can help them reach more people and raise even more money. (more)

Giving can send a social signal, which is useful for encouraging more giving, building communities, demonstrating our generosity, and coordinating with charities. (more)

Influencing people to become effective altruists is a pretty high value strategy for improving the world. … You can do more good with time in the present than you can with time in the future. If you spend the next 2 years doing something at least as good as influencing people to become effective altruists, then these 2 years will plausibly be more valuable than all of the rest of your life. (more)

Yes doing things now can have good side effects, but unless something changes in the side-effect processes, doing things later should have exactly the same sort of side effects. And because of positive interest rates, you can do more later, and thus induce more of those good side effects. (Also, almost everyone can trade time for money, and so convert money or time now into more money or time later.)

For example, if you can earn 7% interest you can convert $1 now into $2 a decade from now. Yes, that $1 now might lend respectability now, induce others to copy your act soon, and induce learning by the charity and its observers. But that $2 in a decade should be able to induce twice as much of all those benefits, just delayed by a decade.

In math terms, good side effects are multipliers, which multiply the gains from your good act. But multipliers are just not good reasons to prefer $1 over $2, if both of them will get the same multiplier. If the multiplier is M, you’d just be preferring $1M to $2M.

Now it does seem that many people are arguing that these side-effect processes are in fact changing, and changing a lot. They suggest that that if you work with or donate to them or their friends, then these efforts today can produce huge gains in inducing others to copy you, or in learning better how to do things, gains that won’t be available in the future. Because they and you and now are special.

I think one should in general be rather suspicious of investing or donating to groups on the basis that they, or you, or now, is special. Better to just do what would be good even if you aren’t special. Because usually, you aren’t.

Now one very believable way in which you might be special now is that you might be at a particular age. But the objectively best age to help is probably when you have peak abilities and resources, around age 40 or 60. If you are near your peak age, then, yes, maybe you should help now. If you are younger though, you should probably wait.

Added 14Apr: Every generation has new groups with seemingly newly urgent or valuable causes. So you need some concrete evidence to believe that your new cause is especially good relative to the others. I am not at all persuaded that today is very special just because some people throw around the phrase “effective altruism.”

Added 19Apr: Since my point doesn’t seem to get through just using simple words, here is a more formal math explanation:

Without loss of generality, we can define help x so that it is time-independent, i.e., so that x gives the same amount of direct help no matter the time t it is given. Also, assume that the process by which direct help x at time t results in indirect help at later times is stationary. That is, for every small x spent at time t, a distribution of gains are produced at later delays s according to the same function f(s). Thus the total help resulting from direct help x at time t is x*(1+Integral_t^Infty f(u-t)*du) = x*(1+Integral_0^Infty f(s)*ds. So if this integral is finite, then direct help x induces a constant indirect help multiplier M = 1+Integral_0^Infty f(s)*ds.

One might define a rate of return r for this indirect help as the r that solves the equation 1 = Integral_0^+Infty exp(-r*s)*f(s)*ds. And this rate of return r might in fact be huge. But note that regardless of the return r one calculates from a formula like this, one always gives more total help by choosing a larger amount of direct help x. So if you can give more direct help by helping later, you should.

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Hidden Asset Taxes Must Be Huge

Paul Krugman:

Piketty’s big idea is that we are in the early stages of returning to a society dominated by great dynastic fortunes, by inherited wealth. … Imagine a wealthy family that has managed, somehow or other, to guarantee that a large fraction of its income is used to accumulate more wealth. Can this family thereby acquire a dominant position in society?

The answer depends on the relationship between r, the rate of return on assets, and g, the overall rate of economic growth. If r is less than g, dynasties are doomed to erode: even if all income from a very large fortune is devoted to accumulation, the family’s wealth will grow more slowly than the economy, and it will slowly slide into obscurity. But if r is greater than g, dynastic wealth can indeed grow to gigantic size. …

Piketty tells us something remarkable: historically, r has almost always exceeded g – but there was an exceptional period in the 20th century, a period of rapid labor force growth and technological progress, when r was less than g. And he asserts that the kind of society we consider normal, in which high incomes reflect personal achievement rather than inherited wealth, is in fact an aberration driven by this exceptional period. … A couple of questions:

1. How much of the decline in r relative to g in the 20th century reflected fast growth, and how much reflected policies that either taxed or in effect confiscated inherited wealth? In other words, how much was destiny, how much wars and political upheaval? Piketty stresses both factors, but never gives us a relative quantitative assessment. (more from Piketty here, here)

This rate of return on assets r that Krugman and Piketty discuss is something like the ratio of rental to purchase price of land. I don’t have access to Piketty’s book, but I’ve been pondering this question for a few months, and I’ve concluded that the usual estimates of asset returns r must fail to include many taxes that in practice reduce the actual rate of return r that growing dynasties can achieve. And I think that once we include all hidden taxes, the actual rate of return r that dynasties could achieve in practice must have usually be no more than the economic growth rate g. Let me explain.

Some taxes are explicit, like property taxes. Other taxes are implicit in the property destruction and transfer that result from wars, political upheavals, and legal corruption, and in the costs of reasonable efforts to prevent such losses. Finally, there are implicit taxes resulting from local legal limits on who one may use to manage a dynastic fund. For example, if a dynasty must give its eldest living male wide discretion over spending and investment choices, and if such males often turn out to be spent-thrift fools, this will greatly limit this dynasty’s ability to grow over the long run. An ideal might be to delegate dynasty management to a reputed professional trust that is legally obligated to follow explicit instructions to grow the fund as fast as possible over the long run. But, as I’ve discussed before, most societies have put substantial legal obstacles before solutions like this.

I argue that the net effect of all these hidden taxes on dynastic funds must have been to usually reduce asset returns to below growth rates. My argument is simple: If asset returns had typically been above growth rates, then if any dynastic funds had chosen to grow at the maximum possible rate, then even if those funds had started small they would have come to dominate investments worldwide. And they would have done so on a timescale short compared to the time period over which historical records suggest that asset returns have exceeded growth rates. By competing with each other, such dominating dynastic funds would then have increased the supply of investment so much as to drive down asset returns to or below the sustainable level, which is the economic growth rate.

I conclude that consistently across space and time, the net effects of all forms of taxes on dynastic investment funds, including taxes implicit in limiting who one may trust not to pilfer those funds, has been to reduce real assets returns to below growth rates. Perhaps well below.

Of course, if the main hidden tax in history has been pilfering by dynasty managers, that can result in a world where such pilferers spend a large fraction of world income, without much social value to show for it. One might easily dislike such a scenario, and want to prevent it. But instead of adding more explicit taxes to prevent the growth of dynastic funds, it seems to me better to cut the pilfering tax. Because this should encourage much more investment overall, which seems a good thing. This includes investment in helping and protecting the future, including protection from disasters, including existential risks. Which also seem like good things.

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Speculators Foresee No Catastrophe

In the latest American Economic Journal, Pindyck and Wang work out what financial prices and their fluctuations suggest about what speculators believe to be the chances of big economic catastrophes. Bottom line: [simple models that estimate the beliefs of] speculators see very low chances of really big disasters. (Quotes below.)

For example, they find that over fifty years speculators see a 57% chance of a sudden shock destroying at least 15% of capital. If I apply their estimated formula to questions they didn’t ask in the paper, I find that over two centuries, speculators see only a 1.6 in a hundred thousand chance of a shock that destroys over half of capital. And a shock destroying 80% or more of capital has only a one in a hundred trillion chance. Of course these would all be lamentable, and very newsworthy. But hardly existential risks.

The authors do note that others have estimated a thicker tail of bad events:

We obtain … a value for the [power] α of 23.17. … Barro and Jin (2009) … estimated α [emprically] for their sample of contractions. In our notation, their estimates of α were 6.27 for consumption contractions and 6.86 for GDP.

If I plug in the worst of these, I find that over two centuries there’s an 85% chance of a 50% shock, a 0.6% chance of an 80% shock, and one in a million chance of a shock that destroys 95% or more of capital. Much worse chances, but still nothing like an existential risk.

Of course speculative markets wouldn’t price in the risk of extinction, since all assets and investors are destroyed in those events. But how likely could extinction really be if there’s almost no chance of an event that destroys 95% of capital?

Added 11a: They use a power law to fit price changes, and so would miss ways in which very big disasters have a different distribution than small disasters. But to the extent that this does accurately model speculator beliefs, if you disagree you should expect to profit by buying options that pay off mainly in the case of huge disasters. So why aren’t you buying?

Those promised quotes: Continue reading "Speculators Foresee No Catastrophe" »

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