Inflation basics [Econ for poets]

I recently had a conversation with a smart acquaintance about monetary policy, and we discussed the new Bank of Japan’s governors’ promises to push for higher inflation in the country. I tried to argue that we had good reasons to believe that such an inflationary policy could boost the real economy, while my friend argued against me. But eventually, I realized that the friend and I were doing a bad job articulating what, exactly, drives inflation, and this was a drag on our conversation. I suspect that there are a lot of us who know how to use all the words we see associated with inflation in magazines (“money supply,” “loose monetary policy,” “inflation expectations,” etc. etc.), who may even remember a mathematical formula from Intro Macro (MV = PQ), but who, when we dig a little deeper, have to admit we don’t have a clear grasp on what’s going on. So I thought I could do the blog world a favor by writing a very back-to-basic post (in English words) on what inflation is exactly and how it happens.

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What is inflation? It is a rise in the prices of goods and services. What causes inflation? Most people would say that  inflation is driven by an increase in the amount of currency or money in the economy — the “money supply.” The intuition here is that if an economy produces the exact same amount of goods in year 1 as in year 2, but there is twice as much money in circulation in year 2, then prices will have to double in order to sort of “soak up” the extra money. I think that’s the implicit metaphor most of us have for how it works: The monetary price of real goods is determined by the amount of money in circulation relative to the amount of real goods; and inflation (and deflation) is driven by increases (and decreases) in the money supply. Now, the interesting thing about this is that it is mostly true in practice but not entirely true in theory. To get a much better grasp  on this, we need to go back to very basic theory, to make sure we’re clear on things, and then we need to clarify exactly what we mean by the “money supply.”

Who sets prices? Theory: In a market economy, everybody sets prices. That is, the price of anything in a market economy is the price at which sellers choose to sell their goods, provided that they can find buyers. So any full explanation of inflation has to answer the question: Why, exactly, did sellers choose to raise their prices and why did buyers go along with it? So let’s start with an incredibly simple model: Adam and Barbara are stranded on a desert island and they have their own economy. Adam grows peaches on his peach tree; every day, he harvests a bushel, eats a peach for himself, and sells the rest to Barbara; Barbara then eats a peach, turns the rest into peach juice, drinks some of it, and sells the rest back to Adam; Adam drinks some of the peach juice and uses the rest to water/fertilize the soil of his peach tree. One day, a $10 bill falls from the sky. Adam and Barbara decide to use this for their transactions: First, Barbara gives Adam the $10 bill in exchange for his peaches; then Adam gives Barbara the $10 back for her peach juice.

Now, suppose that two more $10 bill falls from the sky, one into Adam’s hand and another into Barbara’s. What will happen? Will prices triple? Well, that’s up to Adam and Barbara. They might just decide to save their new $10 bills and continue trading one day’s worth of peaches and one day’s worth of juice for $10, every single day — the only thing that would have changed from before would be their “savings.” But it also is possible that prices could increase. Maybe one day Adam gets greedy for dollar bills, and decides to demand $20 from Barbara for his peaches — he knows she has the money, and since he’s her only supplier, she has to consent. At that point, since Barbara now expects she’ll have to pay $20 for future supplies of peaches, she’ll start charging $20 for a day’s worth of peach juice in order to maintain her living standard. So suddenly prices double, just like that. And it’s also possible — this is the really interesting part — that prices could more than triple. Perhaps Adam gets really greedy and starts to charge $40 for his peaches — more than all the currency in the economy — and Barbara responds by charging $40 for her peach juice as well. One way this could work is that, first Barbara buys half a day’s supply of peaches for $20, makes half a day’s supply of peach juice and sells it for $20, and then uses that $20 to buy the next half-day’s supply, etc. Another way they could do this would be to use the magic of credit —  Adam or Barbara hands over $20 for the full amount of peaches/peach juice and also a promise to pay another $20 that night. At the end of the day, after their two transactions, each is $20 in debt to the other, but each earned $20 in cash from that day’s transaction, so they simply swap $20 to settle up.

Now, notably, this simple model is not a good a good one, because it leaves out (1) the reason money is useful and influences our behavior in the first place, namely that is completely fungible and usable across a broad array of transactions that would otherwise be complicated by barter and (2) competition, which is the major thing that stabilizes prices in the first place. But the point of this model has been to get us beyond our implicit metaphor that prices have to “soak up” the supply of money. Adam and Barbara — the market — are in charge of the prices they set, and they do so according to their own purposes. They could randomly double or halve their prices at their whims. And what’s true for Adam and Barbara is also theoretically true for all of us. If every single person in the world were to wake up in the morning and decide to double the prices they pay and charge for absolutely everything (including doubling, e.g. the amount of credit they demand from and extend from others), then this could work without a hitch — every numerical representation of the value of every good would change, and nothing else would.

The above is just a verbal expression of the familiar “Equation of Exchange” that we see in Econ 101, MV = PQ. In this equation, P represents the price level and Q represents the total quantity of real goods sold — multiplied together, PQ thus simply represents the nominal value of all real goods sold in a given time period. So in the second iteration of our fictional desert-island economy above (where Adam and Barbara were each charging $20), PQ = $40 per day. What about the other side of the equation? M represents the supply of money (a total of $20 in that part of the thought experiment). And V is stands for velocity of money, or the number of times any given unit of that money changes hands in a transaction, per time period; in our thought experiment, since $40 worth of goods changed hands a day, and the amount of money was only $30, then the velocity of money was 1.333 transactions per day (($40 of transactions/day) / $30). If you think carefully about this, you can see that MV = PQ is an axiomatic mathematical identity: The total monetary value of all transactions taking place in a given period of time must necessarily be equal to the amount of money there is times the number of times the average unit of money changed hands in a transaction. If prices suddenly double, while everything else stays the same, it must necessarily be the case that money is changing hands twice as fast, doubling V.

So let’s now think about some of the things that happened in our thought experiment, in terms of this identity, PQ = MV. At first, there was $10 in the economy, and $20 worth of purchases, because the $10 bill changed hands twice a day. So PQ = $20 and MV = 2 * $10. It balances! Then $20 fell from the sky. In one scenario, Adam and Barbara didn’t change their prices, so PQ still was equal to $20. Since M was was now equal to $30, V must have fallen to 2/3rd. In other words, since they were still just doing the same transactions, at the same dollar value, even though there were two new $10 bills hanging around, the ‘velocity’ of any given $10 bill was now 1/3rd of what it had previously been — only 2 $10 bills changed hands per day, even though there were 3 of them in the economy. In the scenario after that, both Adam and Barbara raised prices to $40, meaning that PQ was now equal to $80. Because M was equal to $30, V was necessarily 8/3 transactions per day — that is, the average $10 bill changed hands more than twice, because of how Adam and Barbara transacted four times per day.

So going forward, let’s keep in mind this main theoretical takeaway: The only fundamental constraint on prices is the mathematical identity that PQ = MV. So, if the money supply, M, say doubles, that could cause prices to double, but it’s also possible that the extra money could get “soaked up” by a lower velocity of money, i.e., people choosing, for whatever reason, to hold on to any given dollar in their hands for longer before spending it (and it’s also possible that we could see a little bit of each, or that velocity could surprisingly increase, leading to more than double inflation, etc., etc., etc.)

What influences prices? Practice: In theory, the only certainty about the price level is the identity that MV = PQ — the velocity of money could double one day, and halve the next, making prices double and halve in turn. But in practice, things are much different. First, we don’t, in practice, all just wake up in the morning and all collectively decide to double or halve the velocity of money. If I own a shop and I double my prices one day, my competitors probably won’t, and so all my customers will leave me and buy from them. If I suddenly halve my prices, I’ll run out of goods real quick and won’t make a profit. So, because most firms (hopefully!) face real and prospective competitors and don’t like selling things at a loss, the velocity of money, V, doesn’t just randomly, wildly oscillate on its own. This means that if both the quantity of real goods an economy is producing, Q, and the money supply, M, are held relatively constant, then we won’t usually see wild fluctuations in the price level, P.

And second, in practice, changes in the supply of money do not usually get entirely absorbed/cancelled out by changes in the velocity of money. Just think about it: If you suddenly had an extra $100,000 would you hide it all under your mattress? Maybe you would hide some of it (you would probably save much — but these savings would be someone else’s credit, which we’ll get to later), but probably you would increase your spending at least somewhat. And if all of us suddenly got an extra $100,000 we would all probably start to spend a bit more. Since our increased spending would amount to an increase in nominal demand for goods, we would expect prices to rise. So the Econ 101 explanation here is that increases in money lead to an increase in nominal demand, which causes nominal prices to rise. If you prefer narrative to graphical style thinking, think of it this way: if we helicopter-dropped an extra $100,000 into everyone’s bedroom, workers would demand higher pay to work overtime (since they already have such great savings), people would take vacations and bid up the price of spots at restaurants and on airplanes, everyone would be willing to pay more for houses, bidding up prices, etc., etc. But people also would hold onto or save much of that $100,000, meaning that velocity of any given dollar would slow down at first, and so the extra money supply wouldn’t be immediately ploughed into higher prices. So usually the price level should correlate and moves with the money supply, but not immediately in a perfect, linear 1-to-1 relationship.

What is money? In the first few iterations of the desert-island thought experiment, “money” basically means “paper currency.” But in the modern world, most of what we call “money” is actually just debits and credits in bank accounts. For example, if you have accumulated $10,000 in cash at work, and you put that into a checking account, you still have $10,000 in “money” (because you can withdraw at any time) even though your bank is not keeping those $10,000 locked away in a vault. Your bank likely lent most of those $10,000 in cash out to somebody else, and so now there is $19,000+ in “money” resulting from your deposit, even though there was only $10,000 in cash. Indeed, if the person who got that loan from the bank spends her $9,000 to hire somebody a job, and that hiree then saves his $9,000, and the bank then loans out those $9,000 in cash to somebody else, then there is now $28,000 in money. As we can see, in the modern world, “money” is very different from “currency,” and so economists have very categories for measuring the money supply. “M0” refers to actual physical currency in circulation; “MB” (the Monetary Base) refers to currency in circulation, currency stored in bank vaults, and Federal Reserve credits to banks (see below); “M1” refers to currency, bank deposits, and traveler’s checks; “M2” includes savings accounts and money-market accounts as well; “M3” includes all those and a few other savings/investment vehicles. As you can see, M0 through M3 are ordered according to their relative liquidity — M0 is just actual cash, which is completely liquid, and M3 includes things that might take a bit more time for you to withdraw — savings accounts and money-market funds. Money, in the modern world, exists on a spectrum of liquidity. Indeed, it’s arguable that ‘money’ in these traditional categories is too conservatively defined. If you have $10,000 invested in an index ETF, and you can exit the ETF at any moment, you might think of those $10,000 as your money, but the Federal Reserve, at least when it pays attention only to M0-M3, would not.

So how does the Federal Reserve control the money supply? It doesn’t do so by “printing money,” as Fed-skeptics often put it — it’s even more aerie than that! The Fed actually mostly influences the money supply just by entering credits and debits into its and other banks’ digital balance sheets.  Suppose a bank has $100 in deposits from savers like you and me, and it has loaned those $100 to General Electric. At this point, there are $200 ($100 in deposits, and $100 in cash on hand for GE). But now, the Federal Reserve can buy GE’s debt obligation from the bank; the bank thus gets $100 (or whatever the market purchase price of the loan was) in cash credit from the Federal Reserve, which it can then loan out to another company, like Ford. So now there’s $300 of money in the economy ($100 for GE and Ford each and $100 for the banks’ original depositors), with the extra $100 having been created simply by the Fed crediting another bank’s account.

In reality, due to ‘fractional reserve banking,’ each purchase of X that the Federal Reserve makes creates much more than X new money, because banks often lend to other banks, or banks’ loanees deposit some of their loans in other banks, etc. So the Federal Reserve can have a large impact on the money supply simply by purchasing banks’ assets — by giving these banks fresh money, it allows them to lend more money to other people/banks who will lend to other people/banks who will lend again, creating new money at each iteration.

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I hope this is all the basic background one needs to understand the talk about inflation that we see in the business press. But I want to quickly touch on some implications:

1. This reason all this theory is important is that it explains why Federal Reserve policy is controversial and debatable. If there were a simple, linear relationship between the money supply and the price level, there would be no controversy — we could easily and uncontroversially predict inflation by quantifying the money supply. But Fed policy right now is controversial, for some, because we can’t actually be sure how changes in the money supply will affect inflation over the long run. It’s theoretically conceivable that a central bank could increase the money supply while observing very little inflation, because people largely hide their new money under their mattresses, only to see that 5 years later, everyone suddenly starts spending their mattress-savings, sending prices skyrocketing. The complex psychological factors that influence the velocity of money, including self-fulfilling expectations about inflation (see below), mean that there is always some uncertainty about what the consequences of the Fed’s actions will be. For the record, I’m not very worried about the prospect of very high inflation. The market’s expectations for future inflation are priced into price difference between TIPS (Treasury Inflation Protected Securities) and regular, non-inflation protected Treasuries. And TIPS continue to show low inflation expectations. If I were smarter than the market, I should probably be a billionaire right now. People who are very certain that high inflation is coming should put their money where their mouths are, by putting most of their savings in inflation-protected securities.

2. Expectations for inflation are largely self-fulfilling: If you expect wage rates to rise 10% next year, you might try to lure a new hire with a contract at a 8% premium (relative to current wages), to lock her in at a price that will be a 2% savings relative to what you expect for the future. If you expect prices for your supplies to rise next year, you might raise prices on your merchandise right now, in order to earn enough cash to afford those higher-priced supplies. If you think your competitors are raising their prices right now, then you know you can raise your prices without losing customers. Etc., etc., etc.. The fact that inflation is a sort of self-creating phenomenon, ultimately based on everyone’s best guess about what everyone else thinks about what everyone else thinks about how much prices will rise in the future, is one thing that sometimes makes it hard to control. Most episodes of hyperinflation ultimately originate from governments printing massive amounts of new money — but from there, inflation radically outpaces the printing presses, as everyone keeps raising prices in response to everyone else’s price hikes in a downward spiral. More, one of the most effective ways for the Fed to control inflation is for the Fed chairman to literally make statements — in words — about future inflation. If the Fed says, “we are committed to ensuring that inflation is 3% next year,” the average company will have a good reason to raise prices by 3%.

3. Most mainstream economists believe that moderately higher-than-usual inflation can help boost an economy out of a recession. There are at least four mechanisms through which inflation can benefit a recessionary economy:

          (i) If you own a company and you expect prices to be 8% higher next year, all else equal that fact will make you more inclined to purchase more merchandise now, while prices are still lower. You also might ramp up your production and investment right now, so you’ll be well-position to meet that high nominal demand.  This boost can help an economy get out of the recessionary downward spiral in which low demand and low production begets more low demand and low production.

          (ii)  Most of us think about our salaries in nominal terms. Most of us do not like to take paycuts. However, during a recession, individual workers’ productivity decreases (i.e., if I’m a car salesman, I’m worth more to my company during a time when lots of people want to buy cars). The problem is that if workers’ contribution to companies’ bottom lines decreases, but workers’ salaries stay the same, then firms will hire less and fire more, and/or become less competitive. Inflation allows firms to lower their employees’ real wages, without needing to lower their nominal wages. Economists think this is a good thing — the alternative to lower real wages during a recession is mass unemployment and bankruptcy.

          (iii) Inflating a currency typically devalues it relative to other world currencies. If we make the dollar worth less relative to the Brazilian real, then Brazilians will be able to more easily afford to buy American goods. This should help America’s exporters, which is another thing that can help drag a country out of a recessionary downward spiral. (The flip side of this, of course, is that it will be more expensive for Americans to import things from Brazil — so policymakers have to think carefully through the full industrial implications of a devalued currency).

          (iv) Inflating a currency benefits debtors (at the expense of creditors). If I owe my very wealthy landlord $1 million next year, but prices rise 15% in the interim, then the “real” value of my obligation to my landlord will only be some $850,000. If I as a middle-class consumer am more likely to spend extra money than my ultra-wealthy landlord, then this inflation-driven decrease in my debt/increase in my wealth (and decrease in my landlord’s wealth) will mean greater net demand in the economy. Again, this short-term boost to demand can help jolt an economy out of a downward spiral. You often hear that the problem we’re facing in the U.S. is that, after the financial crisis, everybody tried to “de-leverage” (that is, reduce their debt obligations) at the same time, which led to a “demand shortfall.” (This is often called the “paradox of thrift” — saving more money is good for any individual, but when everybody does it at the same time, it can cause a recession). Inflation can make it easier to reduce our debt obligations, thus weakening the demand shortfall problem that comes with deleveraging.

On the flip side, most mainstream economists believe that in non-recession times, relatively low, stable inflation is good. This is because it’s easier for people to enter into short-term and long-term economic contracts when they can have relatively certain expectation about what things will cost and be worth in the future.

The weird and awful/wonderful economics of taste and contemporary artisanship

This is a post about a weird and interesting space in economic theory, but it starts with a short anecdote.

Today, I went to my local barbershop and sat for an extra half hour browsing terrible magazines so that I could get my hair cut, specifically, by the owner of the place, an older man with blazing white hair and a thick Greek accent that he still retains from his boyhood in Samos. I feel subjectively that I look better when I get my haircut by the owner, as compared to the other barbers. But as a good junior social scientist, I always try to be skeptical of subjective impressions. Objective social science has been very good at obliterating a lot of our pious impressions about the superior quality of goods produced by lofty artisans and craftsmen — in blind taste tests connoisseurs can’t distinguish a fine wine/cheese from an ordinary one, etc. So what about my haircut? Is there any objective basis for my belief that the owner gives me a better one? What could explain my impression?

I have a couple of hypotheses:

#1: My first was that it’s is that it’s totally an illusion and I’ve just been primed by the owner’s foreign accent, old age, etc., to trust him as a craftsman. I.e., perhaps when the other barbers, with thick south Boston accents, cut my hair, prejudice leads me to watch their work with an overly-critical eye. I look in the mirror afterwards seeking to identify their mistakes and misjudgments and find them for this very reason. The owner’s wise-old-man aesthetic primes me to discover the evidence of his excellent good taste when I look in the mirror, and I see it for this very reason.

Is hypothesis #1 correct? Maybe — perhaps even probably. But my girlfriend, who is a fairly unbiased intellect and never present when I get haircuts, has agreed that my haircuts with the old man have been better. So I want to investigate the possibility that his haircuts really do look better. What in turn could explain this?

#2: The main objective difference I can observe in the barbershop is that the old owner of the place uses only scissors, while the other barbers use the modern electric tools. Could this explain the difference? It’d be really easy to say something like this: “Modern electric scissors save time but sacrifice quality. They impose uniform lengths and increments on men’s hair, while a truly good look depends on the layered textures, and smooth, non-discrete cuts that come only from scissors and the experienced judgments of a craftsman.”

This story could be true, but I’m skeptical. The reason I’m skeptical is that in an alternative universe people might be telling the exact opposite story just as plausibly. Supposed we lived in a world in which fine electric-mechanical devices were prohibitively expensive and rare. Scissors were abundant, but electric scissors were a luxury that only elites could afford. In this world, I’d bet that the electric look would be vaunted as desirable and superior. People in this world would probably say things like: “The electric haircut is a huge improvement over its pre-industrial equivalents. It allows the highly trained electric-scissor-certified barber to cut the hair in fine and exact geometries, as opposed to the rough, shabby, hastily layered looks of the past. A buzz cut is chic, crisp art deco on your head. Such a pity that only a few can afford it…”

See the problem? Our story about how the truly authentic scissored haircuts are better sounds nice; but there’s no way to objectively confirm it, so a person who is a critical outsider to our culture would argue that we’re just reverse-engineering a rationalization for our prejudices. If this is true, my impression makes a lot of sense: I don’t like the look my head gets when electric-scissored because of the cultural/affiliational/class-based reactions that have been ingrained into all of us. In my city, the buzzed, electric-scissored look is associated with the military, chains, Budget Cuts, etc. The look of hair cut by scissors, by contract, is associated with people and places that are willing to pay and wait extra to achieve a more fashionable appearance. And so the old-fashioned-scissored look seems more attractive not because of anything inhering in its geometry, but because of associations inhering in our culture and affiliations.

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So the theory here is that there’s a kind of circular process going on: (1) Aesthetics and taste are not objective. (2) Electric scissors take less time and training to operate properly, so haircuts done with them are cheaper. (3) Therefore, aesthetics aside, income-constrained people will be more disposed to get electric-scissor haircuts; the hairstyles of elite people and elite urban areas will disproportionately be drafted by real scissors. (4) Therefore, the culture will come to associate electric-scissor haircuts with low social standing and regular-scissor haircuts with high social standing. (5) Therefore, the old-fashioned scissor haircuts will be upheld as “objective good taste” and self-conscious elites will be willing to pay more and wait longer for them, which will reinforce the distinction.
It is the superior price efficiency of the electric scissors that causes the look they produce to be associated with low social-standing, which causes it to be devalued. A generalization of this insight is that in matters of ‘taste’ (which is to say: in markers of social distinction) democratizing, price-lowering innovations are at least partly self-defeating.

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This basic idea is key to understanding a lot of markets based around taste, cultural affiliations, etc., and is also troubling to the general optimistic picture of how markets work. Normally, we hope markets work something like this: When we all really want and/or need something, we bid up the price of it; the high price attracts entrepreneurs who want to make a lot of money meeting this demand; entrepreneurs uncover new technologies and production processes to make the thing more cheaply; the entrepreneurs compete with each other to market the good, driving their prices down; and so now everyone can get the thing they want on the cheap. See, e.g., automobiles, computers, etc. But for goods whose value comes at least partially from social distinction (i.e., “positional goods”), entrepreneurs can’t do quite so much good for us, because the technology and production processes that broaden access to the good will, ipso facto, reduce the value of the good (and be panned by cultural arbiters as ‘bad taste’). The value that electric scissors could provide to the world has been partially limited by the fact that their efficiency created a new distinction.

I find this interesting purely as a theoretical contrast to classical economic theory: In these domains, technology improves the objective features of a good, but in doing so detracts from its value as a token in human social hierarchies. In the supply-and-demand curves we saw in Econ 101, the demand for a good increases as its price declines; for these positional goods, the relationship is more ambiguous. But beyond theory, there are a couple interesting implications:

(1) Right now, Apple enjoys famously high margins on and earnings from its products. As Apple faces increasing competition and loses market share, it might be tempted to lower its prices, the natural response for any company fighting off competitors. As an economist, I should love this decision — more individuals could buy more great Apple products more easily. But if I were a consultant to the company, I might be hesitant: It seems to me that a large part of Apple’s brand value comes from the price distinction itself. Today, buying a non-iPhone smartphone labels you as someone who’s too eager to save a couple hundred bucks, a gaffe among yuppies. So Apple lowering its prices might not unambiguously raise its sales. What can Apple do? Personally, I think there’s just realistically no way Apple can keep up its current earnings and margins and so the company warrants its very low PE ratio. But this is not what consultants are hired to say.

(2) This theory provides some hope for an “artisanal economy” in the future. The basic idea, which I first heard proposed by Adam Davidson, is this: Throughout human history, improvements in technology have improved human welfare overall, even though technological disruptions caused short-term harm to the workers whom they made obsolete. But now some really smart people are starting to worry that this time is different. Once artificial intelligence advances sufficiently that robots can do literally anything that humans can do, there will be no way that we humans can complement technology and we’ll all start to be replaced by it instead. So who will have jobs in the future? Well, people who are part of protected licensing cartels might: As long as the government says you need to see a human doctor to get XYZ prescription, doctors will still have jobs. The people who own the capital and intellectual property used to make the robots will also still have plenty of income. But what about the rest of us?
Davidson has proposed that the future looks like Brooklyn, NY, in whose hip neighborhoods you can find artisinal offerings of just about anything. How is this economy supported? Mostly by people across the river, in Manhattan, whose incomes are either directly or indirectly tied to financial services. Are artisanal versions of goods better than their mass-produced industrial counterparts? A lot of artisanal foods probably wouldn’t come out ahead in a blind taste test, but artisanal goods in general are useful for us for expressing cultural affiliations and in-the-know-ness, or adding a unique quality to a dinner party or a unique aesthetic to an interior design. Artisanal goods are mostly useful as social tokens. And that’s a good thing. As such, they’re largely protected from competition from technology, because getting them cheap and efficiently is not the point — the point is having the experience of visiting the artisan’s boutique shop in a hip neighborhood, and telling the story of the good when you bring it home. I wonder if the economy of the future will look a bit like the economy that currently crosses the East River: technology does all the real work in satisfying our objective basic needs; the owners of capital and intellectual property earn huge profits as a result; and the rest of us are employed in vaguely creative professions, doing things that objectively robots could do, but which some rich capitalists want a unique human fingerprint on. I will let the reader decide whether that is utopia or dystopia.

Singapore’s Healthcare System

I’m going to caveat this whole post by saying that health policy is not my expertise. (I spend a lot of time reading about economics and policy, and still would have trouble fully explaining the structure of health-care provision in the U.S. — but maybe this is part of the problem with U.S. healthcare?) But I’ve read a number of attractive things about Singapore’s healthcare system, and so I wanted to share my understanding of, and takeaways from, its Platonic ideal.

The basics, as I undertand them, are this:

First, everyone in Singapore has health-savings mandatorily and automatically deducted from their paychecks and placed into high-interest accounts. Since most people’s health expenses are low when they’re young, most people quickly accumulate a substantial buffer of health savings, which continue to compound over time.

Second, when it comes time to go to the doctor, you can pay for many, but not all, things out of this ‘Medisave’ account. Most medically necessary interventions and prescriptions qualify. Checkups for minor and non-life-threatening ailments or prescriptions for drugs that are helpful, but not actually cures for dangers-to-be-insured-against (e.g., an Ambien to help with jet-lag on international travel), might not be. This ensures that people don’t burden their health savings too much with their neuroses and sniffles, but also ensures that, when medical interventions *are* necessary, the money is there. It also requires medical providers to lower their costs to a point where they can actually attract demand in a free market — e.g., if people have to pay the full cost of Ambien, rather than a meaningless copay, you have to lower the price to a point where it’s worth it from an individual’s perspective.

Third, very interestingly, you can ‘donate’ some of your accumulated medi-savings to your family members. This increases your incentive to keep saving more and more and not overspend even if you are precociously and congenitally healthy, and provides an extra line of support to those who are congenitally and precociously unhealthy, provided that they have loving families with some healthy members. (It’s also interesting and heart-warming to me, because in economics we usually think of incentives as working on individuals, but this is an example of incentives working on the ‘extended self’ of a family. It also provides an extra level of ‘socialization of risk’ at the extended family level.)

Fourth, the government offers very low-cost and subsidized catastrophe insurance. This catastrophe insurance is ‘means-tested,’ meaning that if you have a million dollars of wealth lying around, the catastrophe insurance might not pay out even if you get in a car accident that runs up to $40,000 of medical expenses — because while your accident was tragic, you can plainly pay for it yourself. But if you’re middle class and that same accident would bankrupt you and your lifetime Medisavings, the catastrophe insurance would cover it. Catastrophe insurance represents the most basic, important function of insurance — to socialize the risks of unpredictable, rare, and extremely costly events, so that people don’t have their lives ruined by events over which they have no control.

Fifth, there are basic subsidies for the very poor. For some people, the regular required Medisave and catastrophe-insurance contributions are quite costly, and they, and they alone, receive subsidies. This means that the most vulnerable members of society are supported in procuring healthcare, but the median consumer of medical services has no incentive to consume more than is rational from his own cost/benefit analysis. By targeting subsidies at the very poor, Singapore’s health-care system provides universal access without (as we do here in the U.S.) incentivizing the over-consumption of medical resources.

Sixth, the government makes the market for medical services more competitive by enforcing radical transparency. Healthcare providers are required by law to publish their prices for services, in order to enable and encourage people to shop around for bargains. The U.S. system is radically untransparent. If your child has an ear infection in the middle of the night, and you go to an overnight emergency room to pick up a basic antibiotic (which must be a highly dangerous and addictive drug, given that only AMA-certified mandarins with seven years of education are allowed to dispense it!), the doctor who scribbles her signature on the prescription may charge $500. But you never see that cost — it is absorbed by your insurer who incorporates it into the annual costs paid by your employer, which employer has its medical costs subsidized by the government. We are five or six levels removed from the actual costs of our medical decisions, and so it’s no wonder at all our expenses are so irrationally high.

Seventh, at a certain age, Singaporean citizens can pass on what they have remaining in their Medisave accounts into their savings or retirement accounts. That is, once they’ve made it through most of their lives, they are rewarded for their efforts to control costs and allowed to spend the cash on other needs and wants. This simply closes the circle of giving people incentives to keep their costs low and allowing them to make their own tradeoffs about medical vs. other goods.

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This system seems pretty theoretically ideal. It guarantees universal access via subsidies for the very poor and a mandate to ‘Medisave’ on everyone else. It achieves the most basic, fundamental function of insurance via cheap catastrophe insurance. And it keeps the costs on the public very low by relying on strong incentives at the individual and family levels, price transparency, competition, means-testing, and the general principal that individuals ought to bear their own costs for most things. (Ideal theory suggests that it might also be optimal to provide extra incentives for preventive steps — e.g., subsidizing gym memberships to nudge us to be healthier, and less costly, later on. But given that real-world governments are imperfect and subject to corruption and capture, Singapore’s more basic, keep-it-simple-and-stick-to-the-fundamentals approach is probably a better template for real governments.)

Singapore’s system is based around recognizing realities and trade-offs which are unfortunately a “third rail” for politicians to speak of in the U.S. Namely, medical resources are scarce, and health is one good among many that we want to enjoy in life. So, yes, sometimes it is rational to not get this checkup and not to get that prescription. If people knew and felt the costs of their medical services, they would be able to make these trade-offs more rationally. More, insurance adds value when it actually insures — socializing the risks of the irregular, the unpredictable, and the unavoidable. (Auto insurance does not cover the cost of refilling our gas tanks, because that is not what insurance is for.) And the Singaporean healthcare system exemplifies this. I would like an Ambien the next time I travel to Asia, but ‘I would like x’ is not tantamount to ‘it is rational for x to be fully covered by insurance.’ It would be better for society as a whole if I would bear the full cost of my non-clinical sleep aid and if the company that makes the drug were forced to meet me at a price which I myself would be willing to pay.

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One thing that struck me about Singapore’s healthcare system is that in popular political cosmogonies, we posit the ideals of ‘strong active government’ and ‘individual choice and competition’ in opposition to each other. But Singapore’s system could be seen as both more government-driven and more market-driven than its Western equivalents. It begins with a universal government mandate in order to provide a well-defined public good — but then relies on intense competition, individual choice, transparency, simple and understandable rules, and strong incentives, to keep costs low.

This is my way of saying that I think the popular political cosmogony is misleading, and we should have fewer conversations about ‘big government’ in the abstract versus ‘free-markets’ in the abstract, and keep our eyes on the goal of ‘efficient provision of public services’ while being open to intelligent combinations of government mandates and market incentives/competition in achieving that goal. It’s not useful to say that Singapore’s system is characterized by ‘bigger’ or ‘smaller’ government than the U.S.’s — it’s just smarter government.

Anat Admati’s simple proposal for stopping financial crises: Target debt, not assets

Obviously, the financial crisis of 2008, and the subsequent recession and anemic recovery, was a really big deal. Even if we bounce back to 3% GDP growth rates in this year and the next, the second-order and aggregate effects of the financial crisis will continue to drag on American and global economic growth for literally decades to come. Probably the biggest cost of the recession has been high unemployment among the young, which has prevented members of my generation from accumulating skills, experiences, and savings that they otherwise could have — skills, experiences, and savings that could have done much to contribute to our economic future.

So how can we stop a financial crisis like our last one from happening again? Well, to massively oversimplify, the last financial crisis happened because banks had taken out huge amounts of debt to buy assets whose values were tied to the housing market, and the housing market faltered, causing the value of those assets to decline, which left some financial institutions insolvent, fundamentally unable to meet their obligations to others, and all the panic and uncertainty meant that even fundamentally sound banks lost access to credit they needed to hold them over through the crisis. So how do we stop this from happening again? Well, most of the discussion has centered around regulating banks’ assets. Most people want more regulations and stronger regulators on banks asset purchases–passing regulations to require banks to take on less risk and giving regulators more authority to look at their balance sheets and make them change their asset allocations if they’re being too risky.

But there’s a theoretical problem with this line of thinking: Financial institutions really don’t like going bankrupt (though, notably, the policy of Too Big to Fail can cause a problem of “moral hazard” here). They really do their best to find assets that will increase in value over time. Plus, banks these days — for better or worse — employ a lot of the smartest people in the world — economists, physics and math PhDs, etc. — to model what’s happening in the economy, figure out the probable impacts on their assets, and use that to figure out how to help their bank prosper. And this means that it’s not realistic to expect that the next financial crisis will be averted because a few government regulators getting paid $120,000 a year go up to a few Goldman Sachs economists making $5 million a year, and say, “Hey, look, your assets are going to decline in value, and you’re going to go bankrupt,” and the Goldman Sachs economists will say, “Oh, crap, we hadn’t thought of that.”

If that sounds snarky, let me put it more formally: The value of an asset represents the market’s best assessment of the total discounted future expected returns of that asset. To say that “the value of these assets will decline in the future” is an inherently counter-cultural, quixotic, non-consensus prediction, because the market incorporates its predictions for the future into the current market value of assets. If regulators are smarter than the market and can predict the future better than the market can, then they all should have already made billions and billions of dollars doing their own trading by now. (They generally have not.) In other words, declines in the value of assets are by definition unpredictable — so giving regulators power to stop banks from buying assets that they (the regulators) think are unwise purchases will almost certainly not work. To illustrate this basic theory with actual history: In the mid 2000s through 2007, the Fed assured us over and over again that the housing market was no cause for concern — in late 2007, most economists did not think that the U.S. would enter a recession in 2008 (we were already in one at the time). Regulators will not predict the next financial crisis in advance, because financial crises are by their nature unpredictable and unpredicted.

So what else can we do? Instead of giving more power to regulators, could we give more power to formal, unbiased, conservative regulations about the kinds of assets banks can hold, i.e., requiring that they buy relatively higher amounts of very safe assets, like U.S. Treasuries? This is, in my view, a better line of thinking, but not the ideal primary policy approach. Indeed, one could argue that one contributor to the last financial crisis was, e.g., the requirement that banks hold a certain portion of AAA-rated assets, and the ratings’ agencies stupidly giving Mortgage-Backed Securities AAA ratings. Ironically, the fact that banks could formally meet some of their requirements for AAA assets by buying these MBS actually helped drive up the demand for, hence the price of, MBS, which could have occluded and distorted price signals about their riskiness. In other words, ultimately the “more regulation of asset purchases” idea falls to the same argument as the “stronger regulator power over asset purchases” argument — if we knew which assets were risky in advance, they wouldn’t be so risky. Another objection is that we as a society actually do want banks to do plenty of risky investing, in, e.g., innovative but young companies with uncertain but potentially awesome futures. The tech bubble of the late 90s eventually got overheated, but it’s basically a pretty great thing that American capitalism could hook up a lot of brilliant entrepreneurs in California with the money they needed to implement their crazy ideas to change the world. It’s not clear that we’d be better off as a society if more of that money had gone into pushing yields on U.S. Treasuries even lower.

So what do we do instead? The big idea that’s catching on in the econ blogosphere, and which I’ve been persuaded by, is that we ought to stop focusing on banks’ assets per se, and instead focus on how they finance those assets. One way to think about this is that, as I wrote above, we’ll never see the next big decline in asset values in advance — it will always, by its nature, be unpredictable — but we can increase the chances that the financial system will be robust through such a period. How could we do this? It’s simple: If banks financed more of their assets with equity, and less with debt, they would be able to suffer a greater decrease in the value of their assets without becoming insolvent. So we simply force banks to have more equity relative to their debts: we could do this by simply making them reinvest all their earnings (i.e., not pay out any dividends) until they met the desired ratio. This idea is being advocated most powerfully and vociferously by Professor Anat Admati, as in her new book, The Bankers New Clothes.

Let’s step back to make sure we’re all absolutely clear on the terminology here: If I’m a business, every purchase I make is formally financed by either equity or debt. When I first start my business, I invest $10,000 — that’s equity; when I get a $10,000 loan from a bank, that’s debt. When I spend that money to buy up office space and inventory, then I have $20,000 of assets, financed equally by debt and equity (meaning I have a ‘capital structure’ of 1 to 1). If I make $5,000 right away, then those profits count as new equity immediately, and so I have $15,000 of equity for $10,000 of debt. If I pay those $5,000 out to the owner (myself) as dividends, then those $5,000 are in my personal bank account, and longer on the company’s balance sheet, so the company is back to the 1 to 1 capital structure ($10,000 of debt and $10,000 of equity). If my office catches on fire and now my assets are worth only $10,000, then I now have 0 in equity, because I still owe $10,000 to my creditors. If I invite a partner to come share ownership of the company with me, his/her investment is new equity.

In the run-up to the financial crisis (and still today), banks were famously highly ‘levered’; Lehman Brothers’ assets were financed by some 30 times as much debt as equity. This is sort of like buying a house for $300,000, while making only a $10,000 down payment. What’s so bad about taking out all this debt? The problem is that, the more debt/less equity you have, the greater are your chances of bankruptcy. You legally have to pay off your debts regardless of circumstances (your debt does not decrease because you had a bad year) but your equity just goes with the flow of your assets. If my company has $100,000 in assets, with a capital structure of 1 to 1, and our assets then decline in value to $80,000, then that sucks for me and my fellow owners — our equity just fell from $50,000 to $30,000 — but we can still pay off all our debts and remain a going concern. But if we had financed our $100,000 in assets with a leverage ratio of 9 to 1 ($90,000 in debt and $10,000 in equity), then the same decline in the value of our assets would leave us completely insolvent.

When banks are levered up 30 to 1, just a 3% decline in the value of their assets can leave them insolvent, unable to meet their obligations. When lots of banks are levered up this much, even smaller declines in the value of their assets can put them at risk of insolvency, which can, in turn, force them all to sell off assets in fire-sales, pushing down the value of financial assets even further, or cause them to lose access to credit, leading to a self-fulfilling prophecy, financial contagion, and a credit crisis necessitating bailouts, etc. In other words, each bank’s leverage has negative “externalities” on society as a whole.

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Why do banks take out all of this debt? There’s one fact everyone agrees on: One major contributor is the debt bias in the U.S. tax code. Corporations can deduct the interest they pay on their debt for tax purposes, while they cannot deduct the dividends they pay out to shareholders — indeed, dividends get taxed twice, first as corporate profits and then as income for the owners who get them. This debt bias gives banks a relatively greater incentive to take out more debt. It also means, unfortunately, that if we did undertake Admati’s proposed reform without getting rid of the biased tax incentives against equity, banks would see their costs of funding rise, which could increase the cost of credit throughout the economy. (N.B.: She does want us to get rid of the debt bias as a part of her proposed package of reforms.)

But what if we could get rid of the debt bias? Then could we all agree to increasing banks equity-ratio requirements? This is where the discussion gets tricky and contentious. A lot of bankers are arguing that even if we could get rid of the debt bias, higher equity-ratio requirements would be a bad idea, because they would decrease banks’ Return on Investment (ROI), and hence their value. Think of it this way: Suppose I invest $50 million in a bank, and the bank gets another $50 million in loans, and buys $100 million in assets, which appreciate, over the year, to become worth $120 million. The bank needs to pay back $55 million to its creditors ($50 million plus 10% interest), but the other $65 million is all mine. I make a 30% ROI, even though the bank made only a 20% return on its investments, because the bank was levered up. If it weren’t so levered up, I wouldn’t make as much. If the bank had funded all of its assets with a $100 million investment from me, then I would only get a 20% ROI.

And this is definitely, obviously true — when a company is doing well, leverage multiplies the amount it can return to its shareholders, particularly when interest rates are low. The problem is, when the company is not doing well, leverage multiplies how much the shareholders get hurt. There’s a formal mathematical expression of this idea which proves that (in the absence of tax biases), the capital structure  of a company is irrelevant to its value. The math is hard to express, but here’s an easy way to think about it: Suppose a company has a very reliable business model, and so it’s thinking about levering itself up an extra two times, in order to increase the take-home ROI of its owners.  This isn’t a horrible idea, but it’s also not necessary, for a simple reason: If the investors have faith in the company’s reliability, then they could just lever their own investments in the company up, taking out debt to increase their equity stakes, which would have the exact same effect on their take-home ROIs. So the debt-equity capital structure/ratio is irrelevant to the company’s value to its shareholders — it just shifts around the risk.

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One last quick note: A bedeviling misconception is the language that suggests that higher equity-ratio requirements mean that banks will have to ‘hold’ more equity, which will decrease their ability to lend, hence the supply of credit in the economy. This is totally insipid and false. Banks’ loans are assets — equity vs. debt are the way of financing those assets. Banks do not ‘hold’ equity. As soon as I invest in a bank, it can lend that money out. Banks ‘hold’ reserves as the Federal Reserve — but this is not at all affected by, and has nothing to do with, their equity. Admati’s proposals have nothing to do with how much cash banks have to keep in the bank.

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So here’s a three-step process to make our financial system ten times as safe as it is right now:

(1) Get rid of the debt bias in the U.S. tax code.

(2) Require banks to have equity ratio requirements of 20%. An easy and orderly process for getting banks to reach this level would be to forbid them all from paying out dividends (i.e., requiring them to reinvest all of their earnings) until they reach that level.

(3) Let banks make all the risky investments and chase all the profits they want — and next time their bets don’t work out, let their shareholders, and not the U.S. taxpayers of the financial system as a whole, bear the cost.

Here’s all the interesting stuff in Nate Silver’s The Signal and the Noise

I’ve been immersing myself in statistics textbooks and software recently, as a part of a class and my general career interests. So over a weekend ski trip, I took on a lighter version of the work I’m doing by reading Nate Silver’s The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t. Silver has been thoroughly and well-reviewed since his book was published shortly after the presidential election. So I won’t need to introduce him or the basics of what he does. My post will just highlight some of the more interesting, surprising, and difficult-to-articulate stuff in the book, particularly those that are related to topics in economics we’ve already discussed.

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At the heart of the book is a powerful and important idea: The truth about the world, as best as we can understand it, is probabilistic and statistical, but we humans are unsuited to statistical and probabilistic thinking. What does this mean? Let me give a couple of examples. People say things like, ‘Uncle Lenny died of cancer because he was a smoker.’ The unjustified certainty with which we use the word ‘because’ here reveals a lot about how we think. After all, a large fraction of us—smokers and non-smokers alike—will die of cancer. We know with certainty that smoking statistically increases one’s risk of developing cancer, but we can’t say for sure that Uncle Lenny in particular wouldn’t have developed cancer if he weren’t a smoker. A more rational thing to say would be ‘Uncle Lenny died after being long weakened by cancer. As a smoker, he was more likely than the general population to contract cancer, and so it’s likely that his smoking was a significant contributor among other risk factors to his development of a cancer that was sufficient to contribute to his death.’ But that lacks a certain pith. See the problem? We all know that the underlying reality is that a wide variety of different risk factors contribute to and explain cancer, but we humans like to trade certain and definitive statements about linear causation, rather than thinking about a complex system of inputs that take on different values with different probabilities and interact with each other dynamically to produce distributed outputs with certain probabilities. In other words, we humans like to reason with narratives and essences, but the truth of the world has more to do with statistical distributions, probabilities, and complex systems.

Other examples of essentialist thinking are: When we have a hot summer, we often say that it was caused by global warming; on the other hand, global-warming deniers will say that we cannot make any such attribution because we had hot summers from time to time even before the industrial revolution. The most realistic thing to say would be, “global warming is increasing our chances of experiencing such a hot summer, and thus the frequency of them.” Another example: people will say that, “Kiplagat is an excellent long-distance runner because he is a member of the Kalenjin tribe of Kenya.” This ‘because’ is not entirely justified, but neither is the offense that sensitive people take to this claim, when they say things like, “Not every Kalenjin is a fast runner! And some Americans from Oregon are great runners, too!” The most precise way of putting the underlying truth would be, “Kiplagat is an excellent long-distance runner. He is a member of the Kalenjin tribe, which is well-known to produce a hugely disproportionate share of the world’s best long-distance runners, so this is one major factor that explains his ability.”

Why are we bad at probabilistic thinking, and locked into definite, essentialist, narrative styles of thinking? The axiomatic part of the explanation is that our brains have evolved to reason the way they do because these styles of reasoning were advantageous throughout our evolutionary history. We humans have been built as delivery mechanisms for our masters and tyrants—our genes. They encode instructions to make us and or brains work in the ways that helped them (the genes) get passed down through the generations, rather than working in ways that lead us to the strict truth. Probabilistic thinking takes a lot of information gathering and computational power—things that either weren’t around in our evolutionary history, or were costly luxuries. So our brains have evolved mental shortcuts or ‘heuristics’—ways of thinking that give us the major advantages of surviving and reproducing in the probabilistic world, without all of the costs. Our ancestors did not think, and we do not think, ‘Three out of the last five of our encounters with members of this other tribe have ended badly; so we can conclude with X% certainty that the members of this other tribe that we see here are between Y% and Z% likely to have a hostile attitude toward us.’ Rather, our brains tell us, ‘This enemy is evil and dangerous; either run away or fight—look, I’ve already elevated your heart-rate and tightened up your forearms for you!’ I.e., it gives us an essential claim and a definitive takeaway. In the modern age, public authorities say, ‘Smoking will give you cancer,’ which gets across the main takeaway point and influences behavior in important ways, more powerfully than ‘Lots of smoking generally contributes to a higher probability of developing cancer.’

Our brains are also wired to see a lot of patterns, causation, and agency when they aren’t there. As Silver notes, the popular evo-psych explanation for this is that it is more costly to erroneously attribute a rustling in the woods to a dangerous predator, and to take occasional unnecessary precautions against it, than it is to erroneously always assume that all rustling just comes from the wind, and get eaten alive when a predator actually does appear. Since missing a real pattern is more costly than falsely seeing an unreal one, we tend to see more patterns than there really are, and believe that we can discern a predictable pattern in the movement of stock prices, or get impressed by models that, e.g, predict presidential elections using only two small metrics, finding them more impressive than predictions that rely on aggregates of on-the-ground polling. Our basic innate failures at thinking statistically are reinforced by the culture around us, which accomodates/manipulates us (in good ways in bad) by appealing to our need for narrative approaches to understanding the world.

But now we live in the modern age. Our needs are different than they were in our evolutionary history, and our evolved psychology should not be destiny. We need to learn to reason more truly—which means probabilistically and statistically. Silver explores how we have succeeded and failed in doing this with examples drawn from baseball, online poker, politics, meteorology, climate science, finance, etc.

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Why is it so important that we learn to reason probabilistically and statistically?  There are two main reasons. The first is very practical, and the second is more theoretical but ultimately very important. First, we obviously base our plans for and investments in the future around our predictions of what the future will be like. But the future cannot be known with absolute certainty, so we need to make rational decisions around a probable distribution of outcomes. For example, in chapter 5, Silver recounts an example of a flood which the public authorities predicted would rise to 48 feet—since the levees protecting a neighboring area were 51 feet tall, locals assumed they were being told that they were safe. But the 48 foot prediction obviously had a margin of error and, this time, it was off by more than 3 feet, and the levees were overrun. Given how dangerous it is to be in a flooded area, the local residents, had they understood the margin of error in the prediction and the probability of the levees being overrun, would have decided it was worth evacuating as a precaution—but they weren’t made to understand that the authorities’ prediction in fact entailed a range of possibilities. This is a very concrete example of the ubiquitous problem of reasoning, planning, and acting around a single true expectation, rather than weighting a range of possible outcomes.

Another example of this is how climate scientists don’t feel like they can give probabilistic statements to the public, like, ‘The most likely outcome is that, on our current path, global temperatures will rise on average 2 degrees Celsius over the next 100 years, and we have 90% certainty that this increase will range between .5 and 3 degrees. Additionally, we fear the possibility that there could be as-yet-imperfectly-understood feedback loops in the climate which could, with 5% probability, raise temperatures by as much as 8 degrees over the next century–while the chance of this is low, the potential costs are so high that we must consider it in our public-policy responses. Additionally, the coming decade is expected to be hotter than any in the last 100 years, but there is a 10% possibility that it will be a cool decade, from random chance.’ The public—you and I—are not good at dealing with these kinds of probabilistic statements. We demand stronger-sounding, definitive predictions—they resonate with us and persuade us, because they’re what our brains are comfortable dealing with. And a lot of the confusion in public debates surrounding scientific matters comes from our demand for definitive answers, where science can only offer a range of probabilities and influences. Climate scientist Michael Mann was quoted in the book as saying, “Where you have to draw the line is to be very clear about where the uncertainties are, but to not have our statements be so laden in uncertainty that no one even listens to what we’re saying.”

But the second, more fundamental reason for why we need to get better at probabilistic prediction is that offering and then testing predictions is the basis of scientific progress. Good models, particularly those that model dynamic systems, should offer a range of probable predictions—if we can’t deal with those ranges, we can’t test which models are the best. That is, we as a society would be ill-advised to say to climate scientists, ‘You predicted that temperatures would rise this decade, but they didn’t—neener neener.’ Rather, we should be savvy enough to understand that there’s a margin of error in every prediction, and that the impact of some trends can be obscured by random noise in the short run, and so the climate scientists’ claim that temperatures are rising is true even if it did not appear in this particular decade.

The rest of this post consists of some of the more interesting of the book’s ideas about statistical reasoning, and some of the barriers thereto, after a brief discursion on economics.

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I’ve written in the past about the Efficient Market Hypothesis and about the value of short-selling, so it piqued my interest when Nate made some interesting points that related the two. One challenge Nate presents to the EMH is the two ‘obvious’-seeming bubbles that we have experienced in recent memory—the late 90s tech bubble, and the mid-2000s housing bubble. Now, it’s obviously very easy to call bubbles in retrospect, with the benefit of hindsight. But let’s accept for the sake of argument that we really could have seen these bubbles coming and popping—in the 90s, P/E ratios were hugely out of whack, and in the 2000s, housing prices had accelerated at rates that no underlying factor seemed to explain. The question is, why didn’t people short these markets and correct their exorbitant prices earlier?

Well, part of the problem is that in certain markets it can be difficult to accumulate a large short position without huge transaction costs, sufficient to move prices to a more rational level. But Silver’s more interesting argument is that institutional traders are too rational and too risk-averse relative to their own incentives. Counterintuitive, right? What does Silver mean? Let’s imagine that we’re in a market that looks a little overheated. Suppose there’s a market index that currently stands at 200, and you’re an analyst at a mutual fund and you think that there’s a 1/3rd chance that the market will crash to an index of 50 this year. That’s a big deal. But there’s still a 2/3rd chance that the party won’t stop just this year, and the market index will rise to 220 (a 10% return—not bad). In this scenario, the bet with the highest expected return is to short the market, a bet with an expected return of about $.18 on the dollar ( (1/3 * 150 – 2/3 *20) / 200). Going long in the market has an expected loss of the same. So if your goal is to maximize your expected return you go short, obviously.

The problem is, institutional traders don’t have an incentive to maximize their expected return, because the money they trade is not their own. Their first incentive is to cover their asses, so they don’t get fired. And if, in this scenario, you prophecy doom and a market crash, and short the whole market two years in a row, while the market is still rising, you’ll have a lot of outraged clients and you will get fired. And that’s the most likely outcome–the 2/3rds probability that the bull market will continue, and return another 10% this year. If you go along with the crowd, and continue to buy into a bull market that becomes overpriced then, well, when the music stops and the bubble pops, you’ll lose your clients’ money, but you won’t look any worse than any of your competitors. So this may be why a lot of bubbles don’t get popped in good fashion. It’s not that institutional investors are irrational—it’s that they are being rational relative to their career incentives, which are not well-aligned with market efficiency as a whole.

What’s the solution to this problem? Well, part of it is to get more really good short-sellers. One interesting tradeoff here is that the market is most efficient when people are (1) smart and (2) putting their own money on the line. Right now, we’re seeing a transition in which mutual funds and such are becoming more and more common, and so a larger portion of trading that is done in financial markets comes from institutions rather than from individual retail investors. These institutional traders may be smarter than independent retail investors, but they’re not betting their own money, which means their incentives are not well-aligned with market efficiency—the mutual fund’s first incentive is to avoid losing clients, who will bail out if the fund misses out on a bull market in the short term. So institutional investors will face a lot of pressure to keep buying into bull markets even when they know better. In short: don’t expect bubbles to go away anytime soon.

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Silver discusses some of implications of the fact that predictions themselves can change the behavior they aim to predict. This is particularly pertinent in epidemiology and economics. For example, if the public authorities successfully inform the public that, this year, the flu is expected to be especially virulent and widespread in Boston, Bostonians will be especially inclined to get vaccinated, which will then, in turn, cancel the prediction. So was the prediction wrong? Maybe, but thank God it was! In economics, if the economics establishment sees that some developing country is implementing all of the ‘right’ policies, it will predict lots of economic growth from that country—this will cause a lot of investments and optimism and talent to flow into that country which could ‘fulfill’ the prediction. On the most practical level, this means that in these scenarios it’s very difficult to issue and then assess the accuracy of predictions. On a philosophical level it may mean that a perfect prediction that involves human social behavior may be impossible, because it would require a recursive model in which the prediction itself was one of the inputs.

A lot of this reasoning here raises a moral quandary. Should forecasters issue their predictions strategically? We know that public-health authorities’ predictions about how bad a flu outbreak will be will influence how many people get immunizations. The Fed’s predictions about the future of the economy influence companies’ plans for the future, which plans can then fulfill the Fed’s predictions (i.e., if a company is persuaded by the Fed that there will be an economic recovery, then it will ramp up its production and hiring right now, in order to meet that future demand, which will help fulfill that prophecy). Should these and similarly situated agencies therefore issue their predictions not descriptively, but strategically, i.e., with an eye to influencing our behavior in positive ways? In practice, I assume the agencies definitely do. The Fed has consistently optimistically over-predicted the path of the U.S. economy since the financial crisis. This is embarrassing for it, but any cavalier expression of pessimism from the Fed very well could have tilted the U.S. into a double-dip recession. The obvious problem is that when public agencies make their predictions strategically rather than descriptively, they could, over the long run, dilute the power of their predictions in the eyes of the public—i.e., people might start to automatically discount the authorities’ claims, thinking “this year’s outbreak of avian flu, much like last year’s, will affect 10^3 fewer people than the authorities suggest, so I don’t actually need to get a vaccination.”

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Silver offers a lot of helpful reminders that rationality requires us to go beyond ‘results-oriented thinking.’ On televised poker, for example, commentators praise the wisdom and perspicacity of players who bet big when their hands weren’t actually all that strong, statistically speaking, and who win either because (1) they caught a break on the last cards dealt (in Texas hold’em style) or (2) they were  lucky enough that everyone else had even weaker hands. But while commentators may praise these players’ prescience, we should call these bets what they are—dumb luck. We shouldn’t evaluate people’s decisions after the fact using perfect information, what we know now. We should evaluate how rationally they acted given the information they had access to at the time. And betting big with a weak hand, without any information that other players’ hands are even weaker, is never the smart or rational thing to do—even though it will luckily pay off in some chance occasions.

***

‘Big Data’ is a modish term right now. An essayist in Wired claimed a few years ago that as we gain more and more data, the need for theory will disappear. Silver argues that this is just the opposite of truth. As the amount of information we have multiplies over and over again, the amount of statistical ‘noise’ we’ll get will multiply exponentially. With all this data, there will be more spurious correlations, which data alone will not be able to tease out. In the world of Big Data we’re going to need a lot of really sound theory to figure out what are the causal mechanisms (or lack thereof) in the data we have, and which impressive-seeming correlations are spurious, explained by random chance. So theory will become more important, not less.

***

One big takeaway for me, as I read Silver’s accounts for how statistical methods have been applied to improve a variety of fields, is that we are very easily impressed, sometimes intimidated, by mathematical renderings of ideas, but statistics really is not rocket science. The computations that statistical software can do at first seem complex, but they’re all ultimately built on relatively easy, intuitive, concrete logical steps. Same with models: the assumptions on which we build models, and the principles we use to tease out causation and such from within the wealth of data, are ultimately pretty intuitive and straightforward and based in basic logical inference. In reading Silver’s account of the how the ratings agencies got mortgage-backed securities wrong in the run-up to the financial crisis, I was astonished by just how simple the models the agencies were using were. That is, even those of us who like to bash the financial sector still tend to assume there’s some sophisticated stuff beyond our ken going on in there. But Silver reports, for example, that the ratings agencies had considered the possibility that the housing market might decline in the U.S., but continued to assume that defaults on mortgages would remain uncorrelated through such a period. The idea that mortgage-defaults would always exhibit independence—and that the rate of default as a whole could not be changed by global economic conditions—is flatly ridiculous to anybody who takes a moment to think imaginatively about how a recession could affect a housing market. But because the ratings agencies’ ratings were dressed up in Models based around Numbers on Spreadsheets, Serious People concluded that they were Serious Ratings. A lesson for the future: Don’t let yourself be bullied into denying the obvious truths or accepting obvious falsehoods just because they have been formulated in mathematical notation. A seemingly sophisticated mathematical model is in truth a very bad one if its basic assumptions are incorrect.

The lesson here is not that we should eschew statistical methods—it’s that we should get in on the game and improve the models, instead of being cowered by the people who wield them. Indeed, another striking part of the book was Silver’s admission that his own famous political-prediction model on his Five Thirty-Eight blog is not terribly sophisticated—it’s only been so successful because everyone else’s standards in the political world have been so low. And the statistical methods that revolutionized baseball drafting and trading, as recalled in Moneyball, weren’t that sophisticated either—they were just low-hanging fruit that hadn’t been eaten yet.

***

The more polemical parts of the book center on Silver’s righteous claim that pundits be held to account for their predictions. Silver points out that political pundits, like those who appear on the McLaughlin group, regularly get their forecasts wrong in very predictable ways, and never get called out on them or punished. As one who, like Silver, gets angry when people make plainly descriptively untrue statements about the world, I did enjoy his righteous outrage. But I think that in this, he (and I) get something basically wrong—namely, being a political pundit and appearing on the McLaughlin Group are fundamentally not truth-seeking activities, and so their failure to deliver truth should be completely unsurprising and probably doesn’t even qualify as a real indictment in the pundits’ minds. The goal of the people engaged in these activities is not to uncover the truth, but to root for their team. So of course the Republican pundits on McLaughlin group always predict Republican electoral victories, as the Democrats predict Democratic victories. That’s what they’re there for.

More fundamentally, I think Silver under-estimates how uncommon it is for people to think about the world in a descriptive truth-seeking manner. Most of us most of the time are not engaged in truth-seeking activity. Most of us typically choose the utterances we issue about the world on the basis of loyalties, emotional moral commitments, etc.. Thinking about the world descriptively is just not the natural mode for most people. When a Red Sox fan, in the middle of a bad season, says something like, “The Red Sox are going to win this game against the Yankees,” we shouldn’t actually take him to mean, “The Red Sox are certain to win this game” or even necessarily “The Red Sox have a better than even chance of winning this game.” Rather, the real content of his statement is better translated as, “Rah, rah, goooo Red Sox!”  For most people, statements that they phrase as predictions are not a matter of descriptive analysis of the world—they’re statements of affiliation, hope, moral self-expression, etc. The social scientific and descriptive mindsets are very rare and unnatural for humans, and if we’re going to get angry about people’s failures in this respect, we’re going to be angry pretty much all the time.

But I do agree with the basic takeaway from this polemic: Silver wants to make betting markets a more common, acceptable, and widely-expected thing. If we were forced to publicly put our money where our mouths are, we might be more serious and humble about the predictions we make about the future, which should improve their quality. I’ve long relied on Intrade to give me good, serious predictive insights into areas where I have no expertise, and do wish liquid betting markets like it, where I can gain credible insights into all kinds of areas, were more common and entirely legal.

***

A lot of expert reasoning goes into building a good model with which to make a prediction. But what about us general members of the public who don’t have the time to acquire expertise and build our own models? How should we figure out what to believe about the future? Silver provides some evidence that aggregations of respectable forecasters (i.e., those who have historically done very well) are almost always better than any individual’s forecasts. E.g., an index that averages the predictions of 70 economists consulted on their expectations for GDP growth over the next year does much better than the predictions of any one of those economists. So in general, when we’re outside of our expertise, our best bet is to rely on weighted averages of expert estimates.

But there’s an interesting catch here: While aggregates of expert predictions generally do better than any individual experts, this fact depends upon the experts doing their work independently. For example, Intrade has done even better than Nate Silver in predicting the most recent election cycles, according to Justin Wolfers’ metrics. So does that mean that Nate Silver should throw away his blog, and just retweet Intrade’s numbers? No. And the reason is that Intrade’s is strongly affected by Silver’s predictions. So if Silver were, in turn, to base his model around Intrade, we would get a circular process that would amplify a lot of statistical noise. An aggregation ideally draws on the wisdom of crowds, law of large numbers, and the cancelling-out of biases.  This doesn’t work if the forecasts you’re aggregating are based on each other.

Aggregations of predictions are also usually better than achieving consensus. Locking experts together and forcing them to all agree may give outsized influence to the opinions of charismatic, forceful personalities, which undermines the advantages of aggregation.

***

Nate argues, persuasively, that we actually are getting much better at predicting the future in a variety of fields, a notable example of which is meteorology. But one interesting and telling Fun Fact is that while meteorologists’ actual predictions are getting very good, the predictions that they are compelled to present to the public are not so strong. For example, the weather forecasts we see on T.V. have a ‘wet bias.’ When there is only a 5-10% chance of rain, the T.V. forecasters will say that there is a 30% chance, because when people hear 5-10% chance they think of it as an essential impossibility, and become outraged if they plan a picnic that subsequently gets rained on, etc. So to abate righteous outrage, weather forecasters have found it necessary to over-report the probability of rain.

Meteorologists’ models are getting better. We humans just aren’t keeping pace, in terms of learning to think in probabilities.

***

But outside of the physical sciences, whose systems are regulated by well-known laws, we tend to suck at forecasting. Few political scientists forecast the downfall of the Soviet Union. Nate attributes this failure to political biases—right-leaning people were unwilling to see that Gorbachev actually was a sincere reformer, while left-leaning people were unwilling to see how deeply flawed the USSR’s fundamental economic model was. Few economists ‘predicted’ the most recent recession even at points in time when, as later statistics would reveal, we were already in the midst of it. Etc., etc.

***

Silver points out that predictions based on models of phenomena with exponential or power-law properties seem hugely unreliable to us humans who evaluate these models’ predictions in linear terms. A slight change in the coefficients in the parameter can have huge implications for the prediction a model makes if it is exponential. This can cause a funny dissonance: a researcher might think her model is pretty good, if its predictions come within an order of magnitude of observations, because this indicates that her basic parameters are in the right ballpark. But to a person who thinks in linear terms, an order-of-magnitude error looks like a huge mistake.

***

Silver briefly gestures at a thing that the economist Deirdre McCloskey has often pointed out—that our use of ‘statistical significance’ in social science is arbitrary and philosophically unjustified. What is statistical significance? Let me back up and explain the basics: Suppose we are interested in establishing whether there is a relationship, among grown adults, between age and weight—i.e., are 50-year olds generally heavier than 40 and 35-year olds? Suppose we sampled, say, 200 people between 50 and 35, and wrote down their ages and weights, and then constructed a dataset. Suppose we did a linear regression analysis on the data, which revealed a positive ‘slope,’ representing the average impact that an extra year of life had on weight in the sample. Could we be confident that in general, for the population of people between 35 and 50 as a whole, this relationship holds? Not necessarily. Theoretically, there’s always a chance that our sample set is different—by pure chance—than the general population, and so the relationship in our sample cannot be generalized. There’s a possibility that the relationship we observed between age and weight is not a true relationship at all, but was just a matter of chance. And (as long as our sample was truly randomly selected from the population) we can actually calculate the probability of this possibility, using the data’s standard deviation and the size of our sample. In statistics, we call it the p-value, and a p-value of .05 means that there’s a 5% chance that a relationship observed in a sample is just an illusion, produced by chance. In contemporary academe, social scientists by convention will generally publish results with a ‘statistical significance’ of 95%–i.e., where the p-value is lower than .05. But applying this rule mechanically actually doesn’t make much sense. It means that today, a statistical analysis that produces a result with a p-value of .050 will get published, while one with a p-value of .051 will not, even though the underlying realities are almost indistinguishable. There’s no fundamental philosophical reason for setting our general p-value cutoff at .05—indeed, the basic reason we do this is that we have 5 fingers. In practice, this contributes to the rejection of some true results and the acceptance of some false results. If we accept all findings that establish ‘statistical significance,’ then we’ll accept a lot of false results. For example, if a journal publishes 100 research findings, all of which have a p-value of .03, passing statistical significance, we would expect that, on average, 3 of these findings would actually be incorrect, illusions of the samples from which they were built. (This is, by the way, after controlling for the possibility of the data being incorrectly obtained.)

***

On page 379, Silver has what is possibly the greatest typo in history: “ ‘At NASA, I finally realized that the definition of rocket science is using relatively simple psychics to solve complex problems,’ Rood told me.” (I am envisioning NASA scientists carefully scribbling down the pronouncements of glazy-eyed, slow-spoken Tarot-card readers.)

***

The final chapter in the book, on terrorism, was fascinating to me, because with terrorism, as with other phenomena, we can find statistical regularities in the data, with no obvious causal mechanism to explain those regularities. In the case of terrorism, there is a power-law distribution relating the frequencies and death tolls of terrorist attacks. One horrible feature of the power-law distribution of terrorist attacks is that we should predict that most deaths from terrorism will come from the very highly improbable, high-impact attacks. So over the long-term, we’d be justified in putting more effort into preventing e.g., a nuclear attack on a major city that may never happen, as opposed to a larger number of small grade terrorist attacks. Silver even argues that Israel has effectively adopted a policy of ‘accepting’ a number of smaller-scale attacks, freeing the country to put substantial effort into stopping the very large-scale attacks—he shows data suggesting that Israel has been able to thus ‘bend the curve,’ reducing the total number of deaths from terrorism in the country that we would otherwise expect.

***

But the big thing I was hoping to get from this book was a better understanding the vaunted revolution in statistics in which Bayesian interpretations and ideas are supplanting the previously-dominant ‘frequentist’ approach. But I didn’t come away with a sound understanding of Bayesian statistics beyond the triviality that it involves revising predictions as new information presents itself. Silver told us that the idea can be formulated as a simple mathematical identity: It requires us to give weights to the ‘prior’ probability of a thing being true; the probability that the new information would present itself if the thing were true; and the probability of the information presenting itself but the thing still being false. With these three we can supposedly calculate a ‘postperior probability,’ or our new assessment of the phenomenon being true. While I will learn more about the Bayesian approach on my own, Silver really did not convey this identity on a mathematical level, or help the reader understand its force on a conceptual level.

Overall, then, I found the book disappointing in its substantive, conceptual, and theoretical content. A lot of the big takeaways of the books are moral-virtue lessons, like, “Always keep an open mind and be open to revising your theories as new information presents itself”; “Consult a wide array of sources of information and expertise in forming your theories and predictions”; “We can never be perfect in our predictions—but we can get less wrong.” All great advice—but not what I wanted to get from the time I put into the book. The sections on chess and poker are interesting and good journalism, too, but they will do little to advance the reader’s understanding of statistics, model-building, or the oft-heralded “Bayesian revolution” in statistics, etc. But maybe I’m being a snob and wanting more of a challenge than a book could pose if it expected to sell.

–Matthew Shaffer

What good is short-selling? (Econ for poets)

If you follow the business press, you’ve probably seen the raucous unfolding story about Herbalife, a company whose share-price has tumbled then oscillated ever since Bill Ackman, the hedge-fund manager, took a short position in the stock a couple months ago. Ackman has alleged that Herbalife is actually a pyramid scheme — i.e., that its revenue primarily comes not from its sale of actual goods, but from its ‘multi-level marketing’ strategy in which its distributors recruit new individuals to sign up as distributors, and take a portion of that sign-up fee in return. That is, Ackman alleges that Herbalife distributors are only making money by taking one-off payments from new distributors, which is obviously not a sustainable business strategy over the long run (how will Herbalife’s distributors make money once all 7 billion people on earth have been recruited, if it can’t make money by actually selling its goods?). Ackman wants the authorities to investigate Herbalife’s business model. Others, like Carl Icahn, have come to Herbalife’s defense, saying that Ackman’s allegations are misplaced and, more, since these false allegations have unjustly driven the company’s stock-price downward, the company is now a very good buy.

This story will, no doubt (as is every story’s wont), continue to unfold. But I wanted to use this opportunity to explain and explore the basic theory of short-selling in financial markets. Short-sellers don’t have a good reputation with the companies they target for short-selling, or with members of the public who think that short-sellers hurt the companies they target or profit from others’ losses or hurt the market. But I want to argue that short-sellers play a very valuable role. This post will have four basic parts: (1) I will explain what short-sellers do, emphasizing that they do not directly ‘take capital away’ from companies, and therefore do not directly hurt them. (2) I’ll argue that we as a society do not want the stock market just to go up and up as high as possible, but, rather, we want it to be correctly priced. In part (3) I’ll combine points (1) & (2) to argue that short-sellers play a valuable role. And (4) I’ll caveat my roseate view, and acknowledge and address some criticisms of short-sellers.

***

(1) What is short-selling? The basics: Suppose you have a very good reason to think that a stock is underpriced — that, all things considered, it will return more than the market rate in the future. What should you do? Obviously, you should buy it, which amounts to placing a bet on the stock’s rise. Colloquially, we call this ‘taking a long position’ in the stock. Now suppose that, after a few years, other investors fall in love with the stock, and, now, you think it’s overpriced. What do you? Obviously, you sell. But what if you see a stock that is overpriced, but you don’t own the stock in the first place? What do you do? It would obviously make no sense to buy it in order to then sell it — that would just incur two fees from your broker. So what can you do? Is there any way that you can bet on the decline of a stock’s price if you don’t own the stock in the first place (or bet on its decline beyond just selling off all of your shares)?

As you’ve probably guessed…yes, you can short-sell or ‘short’ the stock. How do I do that? Technically, when I short a stock is that I borrow it from someone else for a contracted time and at a contracted price (colloquially, we call this ‘taking a short position’ in the stock). This allows me to profit from the stock’s decline over the period of the contract. Here’s how it works: Say that stock in QWERT is trading for $100 a share. I could pay somebody else $10 to ‘borrow’ their stock for 1 year — if they expect the stock to rise, stay flat, or even only fall a little bit, then this is a great deal from their perspective. Then, I could immediately sell the stock at the market rate of $100. Then, at the end of the year, if the price of QWERT’s stock has declined to, say, $70 a share, I could repurchase the stock at this new, lower price, before returning it to the party I borrowed it from. So I paid $10 to borrow it for the year, sold it for $100, and then bought it back for $70 — I made a cool $20 while effectively investing only $10 of my own money for the year. (Modern markets are sufficiently sophisticated that I don’t actually write up individual contracts to borrow every stock I short — I can do it with a click of a button. But this transaction is legally happening somewhere underneath my click.)

If I’ve belabored this explanation a bit, it’s because I want to make clear a couple of key points: First, a ‘short position’ (just like a ‘long position’) is simply a transaction in secondary financial markets between consenting adult investors that doesn’t directly impact the capital that the company itself can access. A short-sale is the flip-side to any long position in a stock — long investors are gambling that the stock is underpriced, while short investors are gambling that it’s overpriced. What does this mean? Well, first it means that the common financial metaphors that compare short-sellers to sharks and predators are misleading. Short-sellers aren’t hurting other investors without their consent — when you borrow a stock to short it, your counterparty knows exactly what you’re doing, and makes a deal with you anyways, because s/he disagrees with your assessment. And short-sellers don’t directly harm the businesses they target (N.B. I’ll caveat this later). A company gets the equity capital that it needs in order to grow and function from its Initial Public Offerings and other direct share offerings. But as soon as a company sells shares to the public, the money it received on the sale belongs to it. So increases and decreases in the price that those shares trade for in secondary financial markets (i.e., fluctuations in the stock price) have no direct effect on the company’s store of and access to capital. To repeat: The equity that gets traded in secondary financial markets is completely distinct from the equity that is on the company’s Balance Sheet.

So what is short selling? Here’s another way to define it: It’s a legal transaction in which I’m a nice guy who takes the opposite side of two trades, paying a fee to a guy who wants to loan out his share for a year, and selling to a gal who really wants to take a long position in the stock. If I’m lucky, I make a profit on the trades. If I’m not, he and she do. The company’s day-to-day operations are usually completely unaffected by my trade.

***

(2) What do we want the stock market to do? Some theory: When I was a wee lad, before I understood basic financial theory, I thought of the stock market — as represented by the S&P500 index charted on TV screens and newspaper front pages — as a sort of agentic and determined creature, struggling admirably and valiantly and against adversity to move uphill. The S&P 500 chart was, I thought, a measure of the economy as a whole, and the higher it climbed, the better the world was. And wee-me was not the only one to think this way. Indeed, there’s interesting research at the intersection of cognitive science and financial theory that shows how even sophisticated financial commentators imbue their descriptions of stock prices with normative and agentic metaphors — an increase in stock prices is described as “the market vaulted to new heights,” while a decrease is inevitably written up as “another slip in a faltering market.” The basic metaphor that this language embeds and subconsciously conveys to us is: “The market is a self-willed agent, and it is an excellent thing when it ‘rises,’ and a sad thing when it ‘falls.'”

But this is not actually a rational way to think about the stock market. We don’t necessarily want stock prices to ‘climb’ higher and higher. Rather, we just want stocks to be priced correctly. Why? Well, there’s one very obvious and practical reason, and another less-obvious but more fundamental reason. The obvious reason is that when asset valuations just climb and climb, that causes a bubble, and bubbles usually pop, and cause a lot of instability and hell when they do. Bubbles are bad on the way up and on the way down — on the way up, I look at my stock portfolio and think I’m wealthier than I truly am and spend way too much; on the way down, I get upset about how much wealth I’ve lost and become risk averse and don’t buy enough. But is ‘popping’ the only problem with over-high asset valuations? What if we had a magic-wizard policy that could stop bubbles from popping by banning short-selling, etc., to keep bubbles permanently inflated? Would super-high asset valuations be a good thing in this magical world? Even here, economists would say ‘no,’ because even in this magic world, over-high valuations lead to a ‘misallocation of resources.’

What does this mean? Let’s explore a very simple model. Suppose that I have $100 in savings, which I’m considering investing in the IPO of PetApps.com, a new startup website that sells Apps that help your furry friend keep tabs on what other pets in the neighborhood are up to. (Yes, I’m being derisive.) It’s a ‘roaring’ ‘bull’ market that everyone wants a piece of, driving up equity valuations ‘through the roof.’ I realize that the PetApps.com IPO is overpriced relative to its true, fundamental value, but the market has so much ‘momentum’ that I can cash out of my investment while it’s still moving upwards. Should I invest in PetApps.com? Well, the answer depends on who’s asking the question. From my own selfish perspective, I should — I can make a profit by buying and selling quickly during this frenzy. But from society’s perspective, this investment would be a bad thing. Why? Well, when we say that shares of PetApps.com are ‘overpriced,’ we’re saying that, for their cost, these shares will not earn good returns in the future. In other words, capital invested in PetApps.com will not generate as much value as it would elsewhere; more colloquially, the management of PetApp.com is too incompetent to handle all that money wisely. That means that we would all be better off I invested my cash in some company that was undervalued, or in municipal bonds, or even if I just spent it on a vacation now, which would generate income for airlines, etc.

To generalize this thought experiment: At any given time, we have a lot of good options for what we can do with our money. Given that, we don’t just want to just put more and more value into any particular asset, because that would detract from the money that we could use for the other goods. Rather, we want to price each good correctly; this is what economists mean by ‘allocating capital efficiently.’ We as a society don’t just want company shares to sell for high prices per se during their IPOs — we want them to sell for correct prices, providing the company with exactly as much capital as it can use efficiently.

What about the secondary market (i.e, the buying and selling of stocks that you and I can do through E-Trade after the IPO)? As we noted above, trading in the secondary market does not directly effect the capital available to a company. So does it matter to the real economy? I think so. One way to think of secondary markets is as one big ecosystem that supports the ‘primary’ equity markets of IPOs. That is, primary investors in IPOs only invest in the first place because they are counting on the fact that they’ll be able to cash out by selling their shares, whenever they want, into a liquid market. If they made a wise investment decision during the IPO, investing capital in a company that went on to use it to do something transformative (like Apple), they’ll cash out into rising secondary markets, and make a killing. If they invested unwisely, wasting society’s scarce resources on Pets.com, they’ll lose a lot in secondary markets. Trading in secondary markets thus rewards and punishes investors for making efficient and inefficient investment decisions. It’s the ecosystem that is essentially supporting the basic business of getting good investments into good companies.

Also, companies often sell new share issues, well after their IPOs. Those shares will be sold at a price that reflects the total ‘market capitalization’ of company, calculated as the share price times shares outstanding (i.e., if your total market cap is $100,000, on 100 initial shares, and you issue 100 new shares, your 200 shares should now all sell and trade for $500 a piece, since the total value of the company should remain more or less unchanged). And so the same basic principles apply: We want secondary markets to price shares correctly, not highly, in order to prevent destabilizing bubbles, and to properly reward and punish good and bad allocation of capital. A rising S&P 500 is thus only a good thing if the S&P 500 had formerly been undervalued; a declining S&P 500 is a good thing if it had been overvalued. This is why the normative and agentic metaphors that our financial commentators use for the ‘climbing’ and ‘slipping’ market are problematic and misleading.

***

(3) Does short-selling help the market do what we want it to do? An argument: So let”s put the pieces of the puzzle together. How do short-sellers help support the economy? Well, the most obvious and commonly cited way is that they help provide ‘liquidity.’ If you want to buy a stock in financial markets, you’ll need to buy it from a seller — often a short-seller. But the more fundamental good they provide is that they help correct over-valued stocks. Recall that a short-sale is only profitable if the stock price actually does end up dropping, as the short-seller predicts. Short-sellers, by entering the market and becoming sellers, increase the for-sale supply of a stock and decrease the demand for it, bidding its price down. Short-sellers also have an incentive to publicly reveal their short position, to persuade other investors to drive down its stock price. Thusly do short sellers correct the prices of stocks that they believe are overpriced. This provides the indirect good of supporting the efficient allocation of capital, as discussed above. And often it has more direct, tangible benefits. Short-sellers have historically been very good — often better than the official regulators — at sniffing out and exposing accounting fraud and large companies. The threat of drawing the attention of short-sellers can help scare management teams (whose compensation is typically tied to the stock price) into behaving, being honest and transparent with analysts, not paying out lots of company cash for ‘consulting’ from shell companies they themselves (the managers) own, etc., etc. Short-sellers may be the best and most effective regulators the market has.

***

(4) Can there be abusive, harmful short-selling? Some caveats: I’ll admit that my perspective here is generally pretty positive about the value short-sellers provide, and I hope I’ve persuaded my readers to share this general feeling. But I think there are special and marginal cases in which short-selling can be harmful. What are these? The first is, most obviously, when a short-seller is wrong and deceptive about his evaluation of a stock’s true value. Suppose Bill Ackman is completely deluded about Herbalife, and the business is truly sound. If this turns out to be the case, then the stock price will eventually rise again, and Ackman’s hedge fund will suffer greatly, punishing him for his false assessment, and Herbalife will be fine. But what if Bill Ackman, having realized that Herbalife was sound, quietly exited his short position, without telling anybody? He might profit from doing this, since Herbalife’s stock has already dropped a great deal just on his allegations. If this happened, Ackman would hypothetically profit from, essentially, tricking the markets; Herbalife would have wasted a lot of its valuable capital on its legal threats to Ackman, its PR campaign, etc.. The markets would have, in this special case, rewarded the wicked and punished the good. Could this happen? Theoretically, yes, but in practice it’s unlikely. If he did this, Ackman would ruin his reputation and credibility on Wall Street for the rest of his life, which would be very costly to him in the end — so it’s very improbable that he or any other investor of significance would. More, delusion and deception are native to the human condition, and hardly unique to short-sellers. And why should we say that erroneously shorting a stock is so much worse than erroneously boosting a stock?

In theory, as we’ve noted, a short-sale can only profit you if the company’s stock price actually does drop, as your short-sale predicts. So a short-sale does not reward you for just attacking fundamentally sound companies. Are there exceptions to this? One possibility is that there could, in some marginal cases, be powerful self-fulling prophecies. Again, as I’ve emphasized, trading equity and balance-sheet equity are distinct; so short-sellers don’t deprive companies of the capital they need to do business. But my understanding is that for some companies, lending terms and other obligations are tied to their shares’ trading prices.  E.g., a company might be required to pay a higher interest rate on debt if its shares fall below a certain price. Or a bank that has entered into lots of derivatives contracts might be required to post lots of extra collateral immediately if its share price falls; posting this collateral could, in turn, force the bank to sell off other assets in a fire-sale, which would hurt its core business, initiating a downward spiral. These sorts of effects can be particularly harmful in major economic or financial crises — and so sound regulation should guard against these sorts of systemic and spiraling risks.

But we also hear this concern voiced outside of crisis situations. Some people worry about more mundane ways in which this self-fulfilling prophecy can work its evil. I.e., there’s a fear that short-sellers put heavy pressure on management teams to pay too much attention to short-term stock prices, which could cause them to lose track of sound management for the long run. Is there truth in this idea? Honestly, I’m pretty skeptical. On the most basic theoretical level, the value of a share consists in a slice of all the future profits of a company — so placing ‘the long-term value’ of a company in opposition to ‘its short-term stock price’ is a false dichotomy. More practically, it seems that it would require heroic skill to take down a fundamentally sound business just by psyching out the management. I suspect that this ‘concern’ about ‘harmful short-term pressure’ from short-sellers is largely mongered by management teams who aren’t very good at what they do, and want some pre-fabbed catch-phrases to take to the press, so that the big bad mean short-sellers will leave their company alone!

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(5) Things I didn’t just write: This post has not argued that everything in the financial sector,  and our public-policy approaches to it, is a-okay. It has not argued that equity-trading is necessarily the most morally worthy of professions (I will also not say it is particularly morally unworthy). It has not argued that there is no excess of high-frequency trading in the markets. It has not argued that it is no problem that so much intellectual talent in the U.S. is pulled into the financial sector as against, say, engineering or teaching. It has not argued against circuit-breakers to prevent the massive crashes that can come from panic psychology or algorithm-driven trading. It has argued that short-sellers provide a valuable service that is essential to a modern economy, and the language and metaphors we use to describe them are misleading.

–Matthew Shaffer

Without statistics, we don’t know anything; with statistics, we don’t know everything

This will be a short essay in which I’ll try to explain and justify the modern social sciences’ focus on statistical and quantitative analysis. I wanted to write this both to clarify a number of misunderstandings I hear in my conversations with others, and also to explain why I’ve recently become so much more interested in social science, as opposed to the humanities that so animated me in college. I also recently read a short book on the philosophy of modern social sciences, The Logic of Causal Order, to help inform my thinking about this.

To begin: social science and the statistical methods on which it is based have a proper place. Their proper place is to help us gain descriptive (or ‘positive’) knowledge about how the world works. They cannot answer for us normative, moral, aesthetic questions like, “What is beauty? What literature is the most meaningful and powerful? What are our moral obligations to other people? What forms and stages of life deserve protection? What should our and our society’s ideals be?” These are not questions about what the world is; they are questions about what we want it be and how we ought to live as humans. They can’t be answered with descriptive analysis, because they’re not descriptive questions. As such, these questions are the province of the humanities and not the social sciences. I want to concede and insist upon all of this up front, because I fear that the strongest objections most people have to social science comes from those areas where social science collides with fraught aesthetic, moral, and political questions. If social science does pretend to answer questions like these, then it’s not really being social science.

But for most descriptive questions that we need to answer, our best hope for getting a good answer is sophisticated statistical analysis — and we ought to put as many of our beliefs to statistical tests as possible. And most arguments against this idea are poorly reasoned and deeply problematic. They way I want to articulate this position is to argue against my imaginary friend, Bob:

Bob is a local businessman, and a proponent of the idea that statistical analysis and theory are generally rubbish.He believes in the practical knowledge he has accumulated throughout his life. He has experience, not statistics — you can prove anything with statistics, but experience is solid. He gets practical results — no abstract theories required. He knows that he can tell a good wine from a bad one (even though social psychologists have shown that wine connoisseurs fail in blind taste tests), because he tastes the difference every time he picks up a glass. And he also knows quite a bit about business, which is why now, after his long career as a sole proprietor, he hires himself out as a consultant. For example, Bob says he has practical knowledge that “the essence of running a good retailing business is inventory turnover.” When he took over a failing retailing business 5 years ago, he accelerated its inventory turnover to 40 turnovers per year, and the company quickly turned around.

Problem question: Does Bob have the knowledge that he claims? Definitely not. It’s never fun or nice (or very good for your career) to puncture the self-confidence of successful businessmen, but when somebody tells you (s)he has ‘practical knowledge’ with disdain for statistical proof, you should turn away and never hire that person. Why do I say this?

Let’s just start with Bob’s claim about his business knowledge and come at the problem this way: Let’s think very carefully and explicitly about the claims Bob has made. First, he has a historical claim: he increased the inventory turnover rate at his retail business, and the company flourished. Fine. But what should be obvious is that Bob can’t have any confidence that the increased turnover was the cause of the company’s turnaround. Any company’s prospects at any moment are influenced by thousands of factors. Maybe his company turned around because economic conditions improved globally — the recession came to an end and consumer demand increased. Maybe things just got better locally — fracking started nearby or something. Maybe some really socially influential person in town just happened to discover his store and told all of his friends. Maybe some cool innovation in textiles pushed down the costs of the raw materials, and that cost decrease didn’t get passed on to consumers right away. Maybe some other factor had just been artificially depressing demand for the retailer’s ware the year before Bob took over, and that factor suddenly went away in that year. Given all of these possibilities (and thousands more), can Bob say with confidence that he has practical *knowledge* that inventory turnover was the key to his businesses’ success? No way.

But now, let’s give Bob credit and suppose he has thought about all of these factors and accounted for them — he’s fairly sure that, in this case, nothing else changed in the year of the retailer’s turnaround, and nothing else can account for how well it did. So he goes beyond his simple historical account, and stands by his generalization that “the essence of good retailing business is fast inventory turnover.” He advises his consulting clients to turn over their inventory accordingly. What can we say about Bob now? Well, first, despite his claims to the contrary, Bob is now doing theory — he’s made a claim about cause and effect in general between two variables in the abstract. And also — and this is the important part — Bob has made what is essentially a statistical claim, and the logical next step is to subject his claim to statistical scrutiny. Essentially, Bob has said that faster inventory turnover by itself has a positive impact on a firm’s profits. Another way of putting this is that if we had 100 businesses that were similar in all other respects, and half of them sped up their inventory turnover, we would expect businesses in that half to, in general, enjoy more profits and success than the others.

See what we’ve done here? What Bob has phrased as a claim about the ‘essence’ of business we can just rephrase as a statistical claim. And the advantage of rephrasing as a statistical claim is that, in this form, we can test it, and figure out if it’s actually true, as opposed to the folk-wisdom of one man who just happened to have a lucky experience.

So does Bob actually know what he thinks he knows? I’m not sure, but modern social scientists could find out, using the sophisticated, well-justified, well-audited statistical methods the fields have developed. We could dig through lots of financial data of the thousands of retailers in the world; and find a cross-section of firms that are very similar in most respects except for inventory turnover and through regression analysis they could figure out what is the average impact of a given increase in inventory turnover on a firm’s profits. Ideally, we could even find some way to do a quasi-natural experiment, where a set of firms were randomly assigned to increase their inventory turnover — while other variables were untouched — so we could see the impact of that variable alone. We know that these methods are valid, because they’re just applied math, which is grounded in logic. If you think the methods are flawed, there are mathematical proofs that can show you otherwise. None of this is voo-doo or rocket science and it’s not take-it-or-leave-it — it’s just about taking Bob’s claim seriously, explicitly stating the measurable implications of his claim, and then testing to see if they check out. If he’s right, then they will check out: regression analysis will show a positive influence of inventory turnover on profits. If the regression analysis doesn’t show this, then — while it’s still conceivable that he was right about his particular experience — his claim that higher inventory turnover is better for firms in general is just plain wrong, and there’s no way around it. He should accept that his experience was a statistical quirk and that, as such, he’s not justified in advising other companies accordingly. If his knowledge doesn’t generalize — i.e., if it can’t be applied in other contexts — then it’s not at all useful.

So to review: To do better, we need generalizable knowledge. If ‘practical knowledge’ has not been tested systematically (think: pre-20th century medicine), it is highly, highly suspect. If practical knowledge can be tested statistically, it should be, and the statistics must have the last word.

***

That’s the basic idea. Now I just want to argue back against a lot of the complaints about and slogans against statistical methods that I hear from the people around me.

(1) ‘Numbers leave a lot out — even descriptive (positive) things about the world.’ This is a true, good, and important claim. And it’s not something social scientists are oblivious to. When we get good data, we have sound statistical methods for doing the right thing with it. But when we just can’t get reliable measurable data on things, statistics is useless. And this has implications for the limitations of descriptive social science. For example, a big part of understanding why one brand succeeds and another fails probably has to do with highly complex social influence. Like, one cool group of friends decides to like the brand, and they use it, causing other people to think it’s cool, as it slowly catches on. These things are probably have a bigger impact on a company’s success than does its inventory turnover. But we’ve generally had scant ability to measure how cool and socially influential a company’s customers are, or how people’s general vague attitudes over time go from, “What is that?” to “Hmm, this seems like the it thing,” to “Okay, I’m buying this.” These things are all inside people’s heads, and hence hard to measure — but no less important. As such, there’s always the risk that social scientists will — because statistically significant results are what get them published in journals — place too much emphasis, in their understandings of how the world works, on the things that they can measure.

(1a) BUT — I hasten to add this question — who does have knowledge about the things we can’t measure? The social scientists don’t have reliable data they can ply their methods on, but should we therefore conclue that armchair philosophers, magazine pundits, gurus, or academics in softer cultural-studies and media-theory type fields *do* have this knowledge? I see no reason why we should trust any of these people, in lieux of statistical checks. (Indeed, my own bias is to think that if anything social scientists who are trained to think rigorously about social systems probably have more likely hypotheses than any of these other groups.)

(2) ‘Correlation does not imply causation.’  Yep, statisticians and social scientists know this better than anybody else in the world, which is why we have sophisticated methods for distinguishing correlation from mere causation — controlling for priors, path analysis, etc., etc. Believe me — if you’ve thought of it, so have modern methodologists.

(3) A general pet peeve of mine is that many people think that the social sciences have been discredited by some of the incorrect ideas social scientists had in the early 20th century. These same people usually do not think that all of literary theory has been discredited by, e.g., the noxious, wrong and crazy ideas that Freud and Lacan — to name just two thinkers whose influence persists in lit theory — had about, e.g., women. This double-standard is obvious, blatant, and suggests its own correction. And there is another obvious point to make. Yes, many so-called social scientists in the 20th century had noxious and incorrect beliefs. Do you know how we now know those beliefs were wrong and how we have since corrected them? Statistical demonstrations.

(4) But probably the main reason people object to statistical analysis is that, very plainly, they don’t like what the statistics are telling them. I would place this disliking into two separate categories.

(4a) They doubt the descriptive truth of the statistics. For example, suppose your friend says, “I ready XYZ statistics about my home state/some other group I identify with, but I just don’t think that’s correct — it doesn’t jive with my experience.” What do you we tell this person? Well, first, if this person is correct, and the statistics are not right, then statistics itself provides methods for their correction. Indeed, it is only sound statistical methods that can correct bad statistics. If statistics have been incorrectly obtained, this does not reflect upon statistics itself (which provides ample warnings about and methods for dealing with things like selection bias, etc.), but with the researchers. However, if the statistics that our friend does not like actually were well-obtained and are correct then, well, (s)he’s wrong, and the statistics are right, and we just have to accept that, and there’s no way around it. If you don’t like what statistics are telling you, the you should change your beliefs, rather than expecting the truth to change to accomodate your sensitivities.

(4b) They fear the moral or political implications of a particular statistic. This is the tricky space where people confuse and conflate positive (descriptive) and normative reasoning. Let me work with a sort of tense example. Suppose we were to discover new evidence that many of our behavioral traits and mental qualities were relatively strongly determined by age 5 (a finding, by the way, that I doubt will turn up — as one who is learning lots of new fields and techniques at a relatively advanced age, I am a believer in brain plasticity). I think some of my very progressive political-activist friends might object and worry that such a finding could be used to undermine support for public education — they would worry that people would think, “If we’re pretty fixed by kindergarten, what’s the point of, e.g., investing more in good elementary schools for the less privileged?” But I think this would be the wrong way to approach the finding. We could take plenty of other political lessons away from such a study — we could say it strengthens the case for a huge government push for pre-K education, or better care for pregnant, nursing and neo-natal mothers, because it would show how key the first five years of life are and just how much they do to reproduce inequality. In other words, we shouldn’t fear the possible implications of the statistics — we should face them head on, and work with them as best we can. Or suppose some new study were to show that boys are less likely than girls to have very high facility and proclivity for verbal reasoning. Would this undermine the goal of trying to make boys work hard and be confident in their English and Literature courses? Not necessarily. If anything, evidence that boys have less of a proclivity for English could strengthen the case for making an especially hard push to teach them to read literature well — after all, its important to our society that all people, of both genders, develop their language skills, and so if boys are less likely to do it on their own, that urges us to push them harder.

Are there some cases where the kinds of re-interpretations I have suggested won’t work out, and the statistics will inevitably undermine some political or moral end that you support? Maybe. In that case, you just have to change your view. If you cannot make a case for a moral or political goal while simultaneously acknowledging demonstrably true statistics, then your case does not stand up to scrutiny, and you should find a new one.

(5) Finally, many people see social scientists and their statistical methods as overbearing and arrogant, going beyond their proper station. Does social science itself, properly defined, go beyond its proper station?  No. Do social scientists themselves? Yes, sometimes. So I’ll conclude, after the asterisks, with the two things social scientists and their statistical methods cannot do:

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(1) While social science can tell us about effective ways to achieve particular moral goals, it cannot tell us what our fundamental morality should be in the first place. If an economist comes on T.V. and tells us that ‘Our evidence suggests that raising the minimum wage will have xyz impact on the employment rate and government revenues,’ we should listen respectfully. If she tells us that ‘the minimum wage is intrinsically wrong, because consenting adults should be able to enter into economic contracts on any terms that they like,’ she is no longer being an economist, but a political philosopher. If an economist tells us, ‘An engineering degree brings a college graduate a wage premium of xyz in the labor market,’ that is a fact we should be aware of. If she tells us, ‘A liberal arts degree is completely pointless,’ she is being a cultural critic (there could be a value to a liberal arts degree that is unmeasurable, hence outside the purview of social science).

(2) Finally, statistical methods alone are not alway sufficient to establish causal order. When we come in to test a hypothesis with regression analyses, we usually have a vague picture of the mechanics of the underlying system we want to analyze. We have general ideas that ‘A impacts B impacts C,’ and not the other way around. A lot of these ideas about causal order are common sense — ‘B’ cannot cause ‘A’ if ‘A’ happens first, for example. But a lot of them are less obvious. In these cases, statistics can only test for causal orders that are assumed a priori, and these assumptions they borrow from experts in the area they’re studying.

What’s so important and interesting about accounting

In January, I took a very short intensive introductory course to financial accounting. When I first signed up for it, I cringed to think what my old Nietzsche-thesis advisor would think of such a practical and putatively boring endeavor. But I actually – I really mean this and I’m not just writing this to impress some future employer – found it very intrinsically interesting. So this will just be a brief post in which I’ll expend what I learned, by telling you what’s so interesting and important about accounting. After that, I want to give a brief intro to some of the basic ideas and concepts of accounting, partly because I had been frustrated, when I first started the course, that they are not usually explained well to people who are not already familiar with the field.

So first, what is financial accounting? I would define it as the set of rules and concepts we use to prepare relatively simple financial statements that capture or represent important truths about a firm and convey them to outsiders—both what the firm is worth now and what it’s doing on an ongoing basis. This definition suggests both (1) why accounting is important and (2) why I think accounting is interesting.

First, financial accounting is very important because the information that firms convey to outsiders determines whether, how much, and on what terms those outsiders lend to or invest in those firms. We as a society have limited amounts of capital to invest and lend. So we have an interest in that capital being used very wisely. We want it to be lent to or invested in firms that will use it productively, doing innovative and transformative things, and not lent to or invested in, e.g., hopeless companies overseen by feckless managers who are desperate for another lifeline when, in fact, their business models are outdated. The world would be better off today if more capital had gone to Apple and less to Pets.com during the 1990s. We also want investors and creditors of firms – after they have invested or lent – to continue to have an accurate picture of what’s going on inside a firm, so that they can monitor and pressure managers to behave and do well. In this vein of thought, it’s really not an overstatement to say that modern capitalism—in which public companies, owned by outside shareholders, must compete in capital markets to access the capital they need to grow—depends on good accounting. (Lastly: we as a society also increasingly want information about things like, e.g., how a firm is effecting and harming the natural environment, so we can figure out how best to regulate and efficiently abate these costs—this will become a more significant part of accounting in the future.)

Second, accounting is interesting because capturing and representing the truth about a firm is an intellectually challenging and fraught endeavor. The fundamental truth about a firm is actually fairly chaotic—a million different things of varying importance are going on at once—and we need to figure out rules for distilling some simple but accurate summary from this chaos. Accounting is in this sense a philosophical enterprise. The world itself does not label things as ‘assets’ or ‘expenses’; rather, we humans decide which labels we apply to which things; and we set rules for doing so based off of imperfect intuitions and ideas that involve human social ideas like justice (i.e., we want accounting standards that will promote fair and beneficial outcomes for society as a whole via efficiency and transparency) and conservatism (i.e., we want standards that will prevent managers of firms from doing self-servingly optimistic reporting, because we think that people can be selfish). I think it’s also helpful to analogize accounting to statistics, which is also about distilling useful trends from the chaos of data. How tall are women compared to men? Obviously, the fundamental truth is that there are 3.5 billion+ men in the world, each of a different height; and 3.5 billion+ women, each of a different height. Chaos, in other words. But if we have a research project that hinges on the relationship between gender and height, we have to find some simple way to describe the general relationship between these two populations. Statistics provides us with a way to extract out of the real world some useful artifices: The heights of the “average man” and the “average woman” (neither of whom actually exist as real things in the real world), and the standard deviation of each population—four simple numbers that capture most of what we need to know.

That’s why there’s actually a surprising amount of contention in accounting. For example, in the U.S., publicly listed firms issue financial statements according to U.S. GAAP (Generally Accepted Accounting Principles); in the EU, most countries require their firms to use IFRS (International Financial Reporting Standards). The U.S. has been planning, for years, to ‘converge’ its accounting standards with the IFRS—but this convergence has been slowed and stopped at various points, due to ineliminable disagreements. If representing the truth about a company were a simple, scientific enterprise, this would not be the case. Both U.S. GAAP and IFRS are constantly updated to keep up with business and financial innovation. How do firms account for some new complicated financial transaction, when the underlying goods don’t have the words ‘asset’ or ‘expense’ or ‘liability’ branded on them? That’s up to the bodies that oversee GAAP and IFRS—and both bodies pay lots of very smart people very good money to debate these rules all year.

A more practical introduction to financial accounting:

In practice, financial accounting results in the production of four financial statements. These statements are produced by accountants within a firm, and checked (or ‘audited’) by independent accounts outside the firm, and then disclosed in companies’ official public filings, such as quarterly and annual reports. Under GAAP, these four financial statements are: (1) the Balance Sheet, (2) the Income Statement, (3) the Statement of Retained Earnings, and (4) the Statement of Cash Flows. The most important, in my view, are the Balance Sheet and the Income Statement; so I want to just describe the basic concepts of, and sources of confusion around, these two financial statements, and then describe the basic idea of the other two more briefly.

The Balance Sheet

The Balance Sheet is supposed to capture what a firm is worth at a single point in time. It reports the company’s Assets, its Liabilities, and its Shareholders’ Equity. The Balance Sheet is based around the “accounting equation,” which you may have come across: Assets = Liabilities + Shareholders’ Equity. To understand this, you first need to know that Shareholders’ Equity, in accounting, is not the equity that gets traded in stock markets. Rather, Shareholders’ Equity in accounting is an accounting contrivance that is sort of defined as Assets – Liabilities. This means that the accounting equation is a simple identity. I just wanted to clarify that up front, because it tripped me up for two whole days when I first started with accounting, and not every textbook conveys it explicitly. Now, I think the best way to make the accounting equation clear, from here, is to illustrate it with a stylized story:

Suppose you start your own company. At the beginning, you invest $10,000 of your own money. The $10,000 you invested immediately becomes an asset of the company—cash on hand that the company can use. And because you, the owner, have invested this money yourself, and not taken out any loans, that $10,000 in assets is your equity in the company—if you shut the company down tomorrow, you could take the whole $10,000. So when you first invest $10,000 in your own company, the company has $10,000 in assets (cash) and $10,000 in equity, with no liabilities. $10,000 = 0 + $10,000. Get it? Now, suppose your next move is to pay $10,000 up front to rent a storefront for two years. You might think this $10,000 payment is an expense, but on the Balance Sheet, we consider this ‘prepaid rent’ an asset, because you’ll be able to use that storefront over the next two years in ways that will help you earn money. (Prepaying for rent is, in this sense, conceptually similar to buying, say, an annuity—both will pay out income for a set period, so both are assets.) So what did we do to the Balance Sheet and accounting equation? We just changed $10,000 worth of the asset ‘cash’ into $10,000 worth of the asset ‘prepaid rent.’ So the accounting equation is still at $10,000 = 0 + $10,000; on the Balance Sheet, all we did was change the name of the asset. How do we know that the ‘prepaid rent’ is truly worth $10,000? We don’t. In fact, you might hope that you’ve gotten a great deal on this storefront, and it’s truly worth $12,000. But we can’t just let you report the value of an asset at what you think its true worth is—you’ll probably inflate the value of all of your assets if we do. So GAAP requires you to be conservative, and report the value of your assets at the cost of their purchase—and to hold onto the receipt so you can prove it.

Next, suppose that you now need to buy inventory to fill up your store with goods that you can sell. You don’t have any cash left, so you go to bank, get a $10,000 loan and then use that loan to buy $10,000 worth of inventory. What just happened to our accounting equation? Well, inventory is an asset, because you can sell it to generate income, and the loan is a liability, because you’re liable for paying it back to the bank. So now you have: assets of $10,000 in prepaid rent and $10,000 in inventory… a $10,000 liability… and $10,000 in equity. $20,000 = $10,000 + $10,000. The accounting equation is still in balance. Get it? Now, things will get slightly harder. Suppose that over the first year, you sell half of your inventory for $25,000. What do we report at the end of the year? Well, since you’ve sold half your inventory, what you have left is only worth $5,000; in addition, you’ve now used up half the value of your prepaid rent. So those two assets are now worth only $10,000 combined. Meanwhile, you’ve just earned $25,000 in cash—an asset. So your total assets are now worth $35,000. But you still owe the bank $10,000, a liability, so your equity in the total assets owned by your company is now $25,000. And it makes sense that your equity increased by $15,000 total over the course of the year, because you just earned $25,000 by expending $10,000.

So you can see how, here, the accounting equation Assets = Liabilities + Shareholders’ Equity, must always be true, simply because of how we’ve defined the terms. It’s an identity. When you purchase assets in the first place, that purchase must have been financed by either debt or equity; if you purchase a new asset using the income you generated (i.e., reinvesting earnings), then that income had technically flowed through equity (since owners are entitled to profits), and so, again, your assets increase by the same amount as your equity. Hopefully you can use your imagination to see how this identity will still hold up when, e.g., the owner sells her shares to the public in an IPO; or the company has negative income for a year (say, using up $5,000 of a rent expense and $5,000 of inventory, and only getting $8,000 in income–thereby reducing Shareholders’ Equity by $2,000).

I’ve skipped over pretty much all of the actual details about how you put together a Balance Sheet—how you ‘depreciate’ the value of an asset over time, etc. But I hope I’ve conveyed the conceptual gist. The Balance Sheet is supposed to capture the value of a firm at a point in time—what assets does the company control, what portion of the value of those assets is owed to creditors, and, hence, how much of that value do we say belongs to equity owners?

But as I hinted above, Balance Sheet ‘Equity’ is not actually equal to the equity we’re used to—the equity that trades on stock markets at prices graphed on CNBC. In fact, usually they’re radically different. And this fact is a key to understanding the virtues and the limitation of the Balance Sheet. Necessarily, the difference means that investors do not value a company in the same way that the balance sheet does. I.e., the ‘market value’ usually does not equal the ‘book value’; or, investors disagree with the Balance Sheet about what a company is worth. Why is this the case? What accounts for this difference? In my understanding, there are two basic components to the difference:

First, the ‘true’ market value of assets and liabilities is different from their accounting or ‘book’ values, and investors are interested in market values. For example, suppose your company bought an office building in Williamsburg, Brooklyn, for $4 million in 1993. You might be required to value this asset on your Balance Sheet at $2 million right now (the historical purchase price, minus 20 years of depreciation expenses); but because Williamsburg has gentrified and New York City in general has revived so much since 1993, chances are the actual market value of your building is well over $4 million. (Alternatively, if you bought a building in downtown Tokyo during the height of the Japanese real-estate bubble in 1988, chances are your balance sheet overstates the value of that asset—which is one reason Japanese banks keep holding onto old real-estate investments. Similarly, some U.S. banks have been trading at below their book values, suggesting that investors think they will have to recognize losses on many of the assets they purchased before the financial crisis.) So while we have good reason for accounting for assets at their historical cost—namely, stopping managers from over-optimistically over-representing the value of their assets—this requirement means that assets are not reported at their ‘true’ value.

Second, and more importantly, investors do not simply value a company according to how much they would get if the company were to liquidate today. Rather, equity investors are also interested in owning a slice of all of the company’s prospective future profits. And this capability hinges on things like (1) the reputation it’s gained with customers and (2) the margins in the particular market space it’s entering, to name just two—things that are not captured on the Balance Sheet. That is, investors are interested not just in a company’s assets right now, but in its ability to generate income and profits on an ongoing basis into the future. And this, dear readers (thanks for your patience!), brings us to the Income Statement.

 

The Income Statement

The income statement is the financial statement that’s supposed to represent how the company is doing on an ongoing basis—the proverbial ‘bottom line’ refers to the company’s ‘net income’ which is listed, literally, on the bottom line of the income statement. Because net income is calculated for an ongoing basis, the income statement covers a period of time (the last fiscal year, in the annual report), rather than a particular point in time—i.e., it represents a flow, rather than a stock. The basics of the income statement are actually quite straightforward: for a firm, as for you and me, ‘net income’ is just revenue minus the expenses incurred in earning that revenue. The Income Statement just lists all the firm’s revenues, all of its expenses (including things like taxes), and then subtracts, and reports net income on the bottom line. It’s basically that simple. But there are a couple of extra interesting things we need to understand in order to get the significance of the Income Statement:

First, the Income Statement is fundamentally linked up with the Balance Sheet. For example, in each year, when you earn a positive net income (‘earnings’), you either pay out that income to owners as dividends or reinvest those earnings in the company, thereby increasing Shareholders’ Equity on the Balance Sheet. If you suffer a loss in a year, then the loss (by definition) reduces your assets without reducing your liabilities; so the loss is reflected in a decrease in Shareholders’ Equity. This is all laid out explicitly in the Statement of Shareholders’ Equity (see below); but it’s important to understand conceptually how the ‘slice in time’ valuation/financial position of a company  in the Balance Sheet is constantly being ‘updated’ by its flow of profits and losses as reported by the Income Statement. I.e., profits and losses flow through the Income Statement onto the Balance Sheet.

Second, the major counterintuitive thing about the Income Statement is that income is reported on an ‘accrual basis’ rather than a ‘cash basis.’ That is, to calculate your net income for a year, you don’t just subtract the cash you’ve paid out from the cash you’ve received (this would be ‘cash basis’); rather, you calculate the revenues you’ve ‘earned’ and subtract the expenses you’ve ‘accrued’. Let’s illustrate using the example company we worked with above, in the section on the Balance Sheet: At the beginning of the first year, I had paid out $10,000 for ‘prepaid rent,’ right? But on the income statement, we don’t record a $10,000 expense in year 1; we only record a $5,000 rent expense at the end of the year, because this is the amount of the asset that I ‘used up’ in earning my revenues in that year. Similarly, since I only sold half of my inventory during year 1, I only record half the cost of purchasing the inventory as an ‘expense’ on my income statement—because this is all the inventory I’ve ‘used up’ in earning my revenues. Finally, if I were to sell some inventory to a customer ‘on account’ (they promise to pay me in three months), I’ve already ‘earned’ this revenue, and so that makes it into the income statement even before they actually pay up.

Why do we do it this way? Well, there are a couple of theoretical and practical reasons. The most abstract theoretical reason is that if I have, e.g., a promise from someone that (s)he will pay me in one month, this promise is technically a financial asset right now, in that I have already secured a good guarantee of a future cash flow. And so, theoretically, I’ve impacted my company’s financial position (Balance Sheet) the moment I’ve earned the promise to pay from somebody else, and not in the moment when (s)he actually hands over the cash. Since the whole conceptual idea of the Financial Statements is that the Income Statement ‘flows in’ to the Balance Sheet, the Income Statement should reflect that I have earned the asset ‘promise to pay $X,’ right away—it shouldn’t wait for the exchange of one asset (cash) for another (the promise).

The more down-to-earth theoretical reason is that the Income Statement is supposed to give a good picture of what you can expect a company’s typical yearly income to be. If I invest $1 million in a building that I can use to earn $200,000 a year for the next 10 years, it doesn’t make sense for me to report a $800,000 loss this year, and a $200,000 profit for the next 9 years. I’m doing the same basic business in each year, so it would be a better representation of my true yearly income to recognize the building-purchase as an investment, and therefore to ‘allocate the expense’ of it over the next 10 years—meaning that I recognize $200,000 revenues and $100,000 of expenses, for $100,000 net income, for each of the 10 years.

And another practical reason to do it this way is that it prevents certain kinds of opportunistic and deceptive ‘earnings management.’ Suppose that you’re a manager of a company. You’ve had a very good year, but you have reason to believe that things are about to turn sour. You might be tempted, if we used cash-basis accounting, to do some creative accounting: For example, you could purchase all of the inventory you’ll need for next year up front (right now); that way, you would increase your ‘expenses’ in this year, and reduce your ‘expenses’ in the next year, smoothing out your earnings over the two years. That way, next year, your investors might not catch on that, actually, your company is going downhill fast, and so you could exercise your stock options at a high price well into your company’s downfall. Good for you; bad for everyone else. See the problem? Accrual accounting—by forcing managers to ‘match’ expenses to the period in which revenues are earned—prevents some of this opportunistic timing of expenses.

So that’s the basic conceptual gist of the Income Statement. The actual implementation is tricky business. ‘Accrual-basis’ accounting has some big advantages, but the downside is that doing an income statement with ‘cash-basis’ accounting would be a lot easier to control—you could just look at people’s cash receipts. With ‘accrual-basis’ accounting, we need lots of complex and debatable rules about how to ‘allocate the expense’ of various investment-purchases over time. And we can’t just match these expenses to reality using tangible cash receipts. The rules that accountants consequently use can get complex, debatable, and subject to judgment and discretion. This is why accounting is a serious profession involving a serious professional exam, etc.

 

The Statement of Shareholders’ Equity

This is the simplest financial statement, and, in my view, one that doesn’t really convey much extra information, but is just needed to bridge a technical gap between the Income Statement and the Balance Sheet, by reporting dividends, retained earnings, and the company’s transactions with its own owners (new share issues and repurchases). Basically, the Statement of Shareholders’ Equity just explains any changes in the Shareholder Equity figure (as reported on the Balance Sheet) from one year to the next. That figure is effected in intuitive ways by the company’s net income, dividends, and share repurchases/issues. If a firm earns a positive net income, it can distribute those earnings to its shareholders as cash dividends (in which case the money is taken off the company’s balance sheet entirely, because the cash now belongs to whomever it was paid to—the company is a distinct ‘entity’); or it can retain and reinvest those earnings, which increases Shareholder Equity on the balance sheet accordingly. If a company suffers a loss in a year, this detracts from Shareholder Equity directly. So in most years the Statement of Shareholders’ Equity just reports earnings and dividends, subtracts the latter from the former, and adds the difference to the old Shareholder Equity number to get the new Shareholder Equity number. In years in which the company issues new shares, or repurchases outstanding shares, this also shows up on Statement of Shareholders’ Equity.

 

The Statement of Cash Flows

The final statement is the Statement of Cash Flows. What does it do? Well, if we want our financial statements to give a good picture of the truth about a company, this statement should hopefully plug any gaps of information that other financial statements left out. As the name suggests, the Statement of Cash Flows reports the flow of cash in and out of the company over the past year—how much cash did you have then?; how much do you have now?; what accounts for the difference?; where did it all go?; how much went to investments?; how much was paid out in operations? In theory, you can get all of the information that is presented on the Statement of Cash Flows from the other financial statements. But there are a couple of reasons why it is useful to have a separate Statement of Cash Flows that focuses just on this cash information:

First, if you’re doing business with another firm—lending to them, or servicing or selling to them on account—you’ll want to be paid in cash. And since the Balance Sheet and Income Statement are technically based around the inflow and outflow of assets—not just cash—they may not clearly present all of the information you need. For example, suppose you’re in a bank, and debating whether to lend to a hedge fund. The hedge fund might look great on the Income Statement (earned a 30% ROA last year) and great on the Balance Sheet (a debt-to-equity ratio of only 2-to-1). But if the hedge fund isn’t keeping much cash on hand—indeed is paying a lot of it out to post collateral—and many of its assets are illiquid investments in, e.g, Australian timber woods, the hedge fund could easily get into a situation where it just couldn’t summon the cash to make its interest payments to you. Or suppose you’re doing some contract work for a startup firm that earned a lot of income last year, but hasn’t been able to collect the cash from the other firms it serviced—you might worry that, since they can’t turn their ‘accounts receivable’ into cash, they won’t be able to pay you cash for your work. So there are a lot of situations in which outsiders want to know about the cash situation of a company specifically; but the Income Statement and Balance Sheet focus on assets in general, not cash specifically. So the Cash Flow Statement plugs the gap there.

Second, and finally, the Statement of Cash Flows is also useful for monitoring and guarding against a couple of kinds of misbehavior related to imperfections of the other financial statements. When we went over the Income Statement, I explained why the Income Statement reports revenues and expenses on an ‘accrual basis’; when we talked about the Balance Sheet, I explained why it reports asset and liability values at their ‘historical cost.’ The way I think about the design of the Cash Flow Statement is that it is a useful check on the kinds of mischief and abuse that can come from those requirements in those statements. For example, because you must record assets such as buildings at their historical cost minus their depreciation, the ‘book’ value of these assets can be very different from their ‘true’ or market value. This provides a very ripe opportunity for earnings manipulation. Suppose your company’s basic business model is falling apart, and every day you’re losing money on your actual core operations—in this year, you’ll lose $6 million on operations. Suppose also that you own that building in Williamsburg whose ‘book value’ is now $2 million, but whose real, market value is some $10 million. If you sell off that office building, you can report an $8 million ‘gain’ on the sale, which will make up for your $6 million loss on operations, giving you $2 million in positive net income. With this phony liquidation, you can make things look good this year, increasing your assets, and bringing home big net income, even though this business model is clearly not sustainable. But whereas your Balance Sheet and Income Statement will look fine if you use this strategy, your Cash Flow Statement will reveal what you’re doing. The reason is that Cash Flow statements are divided into three separate sections: cash flows from operations (at top); cash flows from investing activities; and cash flows from financing activities. By clearly decomposing cash flows into these three separate categories (as opposed to the aggregation in the income statement), the Cash Flow Statement helps outsiders better monitor the success of your actual day-to-day operating activities.

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This post doesn’t even scratch the surface of the detailed processes through which accounting actually happens. I just hoped to convey the theoretical concepts and an outsider’s appreciation for (a) how the four financial statements work together to represent the truth about a firm and (b) how our economy as a whole depends on that. The big takeaways from this post, I hope, are (1) accounting is interesting, because it involves a lot of complex and philosophical questions about ‘what is the truth about a company’s value, and how do we capture and distill it in a few numbers?’; (2) accounting is important, because the rules we use to convey information about companies’ value will impact which companies we invest in and which management teams we reward with big bonuses, etc., and so it’s a foundational structure for our economy; and (3) learning some basic accounting is worthwhile, because if you really want to understand what’s going on inside a business, beyond borrowing a few lines from the business press, you need to be able to understand a company’s financials and what they reveal—and, more importantly, what they don’t.

Should Apple “Disgorge the Cash”?

Over at Moneybox, Matt Yglesias has a nice summary of and commentary on hedge-fund investor David Einhorn’s attempts to get Apple to raise its stock price by making a credible promise to pay out larger dividends to its shareholders — and Apple’s subsequent refusal.

Basically, as we all know, Apple is one of the most profitable and seemingly promising companies in the world. But its price-to-earnings ratio (i.e., the price of the company as a whole [every one of its shares summed] divided by its yearly earnings) is low — about 10, compared to about 17 for the market as a whole. That is, investors are valuing ownership of the company at only ten times its yearly income, while valuing most companies at around 17 times their yearly income. Why is this? Well, it could be that investors are pessimistic about Apple’s future, thinking that its current high profit margins and market shares will be unsustainable as, e.g., the smartphone and tablet markets continue to get more competitive and mature. But another component of this is probably that investors are frustrated with Apple management’s refusal to return much of its profits back to the company’s owners in dividends and share repurchases. That is, Apple may have a high value, but that value isn’t getting returned to investors fast enough, and so investors are discounting the company. Apple has, instead of returning its enormous profits to investors as dividends, accumulated massive stockpiles of cash. As Yglesias notes, “Apple’s cash on hand represents over a third of its market capitalization, giving them a high cash-to-stock value ratio.”

Why has Apple done this? The main reason we see cited is that the tech world can be pretty volatile and Apple has gone through some very tough times in the past — so the company is holding on to this cash as a “war chest” in case it gets threatened by aggressing companies in the future. The cash could come in handy if Apple needs to beat back some competitor by selling products at a loss. Or if in 5 years, say, interest rates rise and some new technological space opens up, the cash will come in handy if Apple wants to make a huge investment in the new technology, without incurring new interest expenses by borrowing from banks to finance the investment. So it’s understandable that Apple’s management would accumulate cash. But it’s also very frustrating to Apple’s shareholders whose shares would, if the company were trading at the market-average P/E ratio, increase by 70%. (Keep in mind also that, given Apple’s huge market capitalization, its investors probably include you and me, if we’re invested in any sort of fund that tracks the market.) And it’s equally frustrating to those of us who take seriously the moral idea that managers should serve as agents as shareholders, and should return cash that the company does not have immediate use for — rather than withholding it and saying to the markets, as it were, ‘we know better than you what to do with this extra cash.’

Enter the charismatic David Einhorn, the activist investor at the head of Greenlight Capital. His hedge fund owns .12% of Apple, amounting to some $530 million worth of shares (meaning there is $370 million in it for his fund if Apple can start trading at a normal P/E). He’s been going on TV shows, and to Apple itself, with an idea that, he claims, can allow Apple to hold on to its “war chest” in the short-term, while credibly committing (read: legally binding) the company to return cash to shareholders over the long term. Basically, Einhorn wants Apple to issue a large “preferred stock dividend.” Preferred stock is stock that gives the company a legal obligation to pay a certain pre-defined dividend in perpetuity, if the company pays any dividends at all (this is why preferred stock is sometimes conceptualized as a “hybrid” between equity and debt). As a “stock dividend,” these proposed new preferred shares would be issued to existing shareholders (i.e., a “50% preferred stock dividend” would mean that a shareholder who had 2 shares of Apple common stock would additionally be granted 1 share of the new preferred stock). So Einhorn’s idea is that, by doing this, Apple would preserve its cash “war chest” right now while giving each shareholder a legally enforceable commitment to a larger dividend on her preferred shares — this should immediately increase the value of Apple shares, by beating back the skeptics who fear that Apple will never pay out. In Einhorn’s terminology, this would “unlock value” for shareholders, while still allowing Apple to preserve its conservative investment strategy.

Right now, as I understand it, this is all in a sort of confusing legal limbo — Apple made some changes to its corporate charter that, Einhorn claims, were intended to thwart this plan, so Einhorn threatened to sue to stop it; Apple claimed that Einhorn misunderstood what they were doing, and is seriously considering ways to return value to shareholders. It will, no doubt, continue to unfold. But I want to step back and consider this from a broader perspective. What should we want Apple to do? What is the “socially optimal” path? Yglesias writes:

Beyond the investment strategy debate, there’s a great issue here as to whether the “disgorge the cash” mentality that Einhorn reflects is socially optimal. In many cases I would argue that no, it isn’t. Microsoft’s shareholders would probably be better off if, years ago, the company had simply decided to ride out Windows/Office for as long as possible and kick the profits back as dividends. But the social returns to Redmond’s decision to not go gentle into that good night have been large. Microsoft hasn’t made any money creating Bing, but it has done consumers all around the world a huge favor in keeping Google honest. And Google’s fear that Windows Mobile would somehow lock it out of the mobile search marketplace was the initial impetus for the Android project. Android, too, doesn’t necessarily look like a great dollars-and-cents investment for Google, but it’s been fantastic for the broader world. Back in the day, we got R&D divisions like Xerox PARC and Bell Labs out of a similar “waste” of shareholder value.

So from my view, the problem with Apple’s cash is the reverse of Einhorn’s. They’re not wasting it aggressively enough. The company’s fastidious approach means they’re not going to blow $40 billion on trying and possibly failing to bring an autonomous car to market. They’re not investing in the creation of original programming the way Netflix is. They’re not running near-zero profit margins like Amazon. They’re just making lots of money, perhaps because Tim Cook watched The Corporation or read an Introduction to Microeconomics textbook and got it into his head that maximizing profits is what companies do. Which is kind of sad. They’re obviously really smart people and I bet they could make some cool stuff—and even come up with some plausible-sounding reasons for doing it—if they would just relax a bit.

In other words, Yglesias is pointing out that companies that reject a “narrow” focus on shareholder value give themselves the freedom to take on risky, innovative, not-clearly-profitable projects that can benefit society as a whole — like Microsoft giving Google some competition or Google pioneering driverless cars. Apple could, ideally, hold on to its cash and make some incredible investments in some other futuristic technology — say, something that would allow driverless cars to communicate with each other, so that cities can perfectly optimize traffic flows, etc. Now, I take very seriously what Yglesias is saying here — my long-time readers know just how very excited I am about the prospects of integrated driverless cars, an enterprise which is uncertain to do much for Google’s shareholders, but will do a lot for the world. But I want to push back with a couple of arguments, and then open this for debate.

-First, it’s a false dichotomy to say that companies must either “invest in our shared future” or “keep a narrow focus on shareholder value.” Rather, when a company returns cash to its shareholders, it’s putting money in their hands and allowing them to decide what to do with it. They can — and often do — decide to invest that money in some new, alternative startup that will also do a lot for our shared future. Would it be cool if Apple was doing some side-projects to integrate driverless cars with urban grids? Yes. It’d also be cool if I could cash out of my investment in Apple at a higher price, and invest that extra money in some other, outside startup that was doing the same thing. So you can’t just argue that a company should hold onto cash because “investing in our shared future is good” — rather, you have to argue that the company is better at investing in our shared future than are the capital markets. And if you endorse this position, this means you’re privileging the expertise of a small management team over the distributed knowledge of the market as a whole. That’s not a ridiculous idea — but it is, at least, a debatable one.

-Second, I think it’s important to step back and consider not just this particular company but the ecosystem of capital markets as a whole. When Apple went public in 1980, it was able to raise a lot of cash from the public because its investors took seriously the idea that Apple would do its best to give them a good return on their investment. And that IPO, by providing cash to a company that would use it to advance technology that would benefit the world in so many ways, was a great thing for the world. But if many more companies go down Apple’s subsequent alleged path — of failing to do its best to maximize shareholder value — investors may start to discount the price they’ll pay for shares in new companies. That will mean that less capital will make it to promising upstart companies, which would be bad for the world in the aggregate. In other words, the socially-efficient allocation of investment to startup companies in IPOs depends on a business culture in which management teams take seriously their role as “agents” of their investors. So even if in this particular case, Apple could do more good for the world by holding onto its cash, it might contribute to pessimism among investors, in ways that raise the cost of capital for future companies, hurting society in the long run.

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Quick note. The Business Babe, who is generally more practical-minded than I am, just peeked over my shoulder and argued as follows: “Basically, a company should start paying a dividend once its market has matured, when it knows what its cash flow will be like, and won’t need to hold onto cash for new or unpredictable R&D investments. But once its past that stage, a company can’t hold onto investors with promises for the future — it needs to give them a steady return on their investment right now. And tech right now is sort of like the adolescent that refuses to leave home — not moving on to the next stage. The tech boom is over, and tech companies no longer need reserves they used to. And so to continue to hold on to their cash reserves is to be denial of what has happened. Tech companies have to recognize that the tech boom of the ’90s is over. Tech is a mature industry, and tech companies need to start acting like mature companies.”

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So those are just a couple things to consider. Should Apple disgorge its cash reserves? What do you think?

–Matt, blogging boldly from snowed-in Boston

What We Talk About When We Talk About Branding [guest post]

Introductory note: What follows is a guest post from a close confidante of your main correspondent.  I have recently been encouraged by this confidante to both (1) develop my own personal ‘brand’ as an economics blogger, and (2) to occasionally move away from long-winded theoretical expositions, and toward practical, real-world stuff involving actual firms and news items. In the interest of advancing both of those ends, she has submitted this guest post.

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A company’s brand is its image in the public eye. Tied to and built through names, symbols, phrases, and campaigns, a brand influences consumer behavior and allows a firm to command a premium price or a higher market share. In fact, Tide has such an excellent brand and higher perceived value that it has become an alternative street currency in New York.

But, seriously: On its most basic level, a brand’s value can be thought of as the price premium a consumer is willing to pay for it. Other metrics such as brand awareness, customer retention, market share and customer satisfaction can also be used to estimate a brand’s value. But a brand’s value is inherently slippery to quantify as it can fluctuate greatly from botched ad campaigns or other PR disasters, such as product recalls and corporate scandals. Essentially, we know that brands are worth something, we just don’t know how to accurately quantify this worth. However, that doesn’t mean that there aren’t interesting ways we can view branding and the value of brands, especially when brands are put in extreme situations, such as on the auction block during liquidation, or when popular brands represent unpopular (or nonexistent) products.

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It was a difficult year for the legendarily imperishable confection, Twinkie, as its parent company, Hostess, filed for bankruptcy in early 2012. After negotiations with workers’ unions failed, Hostess was forced into liquidation in November. Consumers panicked, clearing shelves and listing boxes of Twinkies on Ebay for (I kid you not) over $200,000.

But this isn’t the story of a failed company. Rather, the most fascinating part of this process has been the forthcoming sale of the Hostess brand names. Brands from the legendary Twinkie to Hostess Cupcakes and Ding-Dongs will be auctioned off to the highest bidder, with potential suitors ranging from Walmart to McKee Foods. Creditors anticipate the sale tipping over a billion dollars, twice the company’s projected worth before liquidation.

The nuts and bolts of Hostess, its trained labor force, management, factories, and specialty machinery, are worth far less than its brands. In other words, the design stamped on a Twinkie’s box is more valuable than the Twinkie itself, the idea of the product surpassing and rising above the product itself. Anyone can make an elongated tube of cake and stuff it with frosting, but only the Twinkie can capture the American heart (arteries included) and with it, the American wallet.

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Let’s push the power of branding forward another step by looking backwards, at the licensing schemes of French fashion houses. As the demand for couture slowed in the mid-20th century, houses began to license their name to third parties to take advantage of lucrative royalty fees. Pierre Cardin infamously allowed his name to be placed on bidets.

Ironically, as licensing schemes proliferated, couture sales continued to fall. French fashion production drifted away from the largest clothing market, America, by continuing to craft inaccessible, exorbitantly priced pieces that arrived too slowly for the modern fashion season. In the very moment when their brand was in highest demand, their couture was universally ignored. French fashion became intertwined in a vicious cycle where their historic couture reputation gave them a valuable brand name, profits came from stamping this name of a variety of third-party products, and unpopular seasons of couture were supported by these royalties. No one wanted French fashion, but everyone wanted French labels.

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And now, dear readers, let’s move from the gilded past to the vulgar present. Twinkie is a case where the brand makes a perfectly simple snack valuable and French couture is an instance where a brand proliferates regardless of the disdain for the product it purports to represent. But I would argue that the pinnacle of branding, the crème de la crème, is the luscious Kim Kardashian. She is the American mega-queen of three-hit reality television shows, a chain of fashion boutiques, numerous lines of clothing, and one fabulously profitable wedding (and divorce) to Kris Humphries. But whereas Lance Armstrong, beneath his tarnished brand, represents athleticism and resilience, and while Justin Bieber offers his earnest, teenage croon, Kardashian is ostentatiously talentless. In an interview with the Guardian, she noted, “When I hear people say [what are you famous for?], I want to say, what are you talking about? I have a hit TV show.”

Her logic is circuitous; her fame justifying her fame. Transforming her life into a brand, she represents nothing except desire, price premium, and superiority. When you consume Kardashian, there is no utility beneath the brand and therefore no substitute—no generic alternative is possible. She is pure brand, pure fame, a slick white line of our powered need.

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This is why this and every blog like it needs a brand. Without one, your Econ Blogger is merely a mind to readers. Similar to his minimalist WordPress theme, his words and paragraphs stand naked and open for judgment. Without the direction, the cushioning, the gentle shaping that a brand lends, he is yellow-sponge cake that must, on every read, be evaluated by its own, spongy merits.

There are intellectual benefits to this, of course. You may feel more open to criticize his work because of your lack prior investment.  You may be more receptive to his writing, not having seen an unflattering picture of him that may have biased your engagement. But the harms of this type of semi-anonymous work are far greater. Loyalty is built through branding, market share is gained by positing yourself not just as the best, but as fundamentally different, intriguing, intimately compelling. Engaged readers lock in and comment when they feel personally involved. If he wants to be remembered, he (along with all other WordPress blogs) needs more pieces that involve a Self, and some selfie shots, too—selfies are key.

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One last word of caution. The main correspondent on this blog has legendary, dazzling, world-renowned blond hair, which he, in his public appearances, combs and folds into a cavalier and winsome side-part. Some critics have complained that it is too handsome to be endured and threatens the morals of the youth. There is, therefore, the danger of brand eclipsing the product, which may be why mdsy10 is nervous about taking this direction.