Growth Diagnostics: A Crash Course

In the mid-2000s, Ricardo Hausmann and Dani Rodrik developed a growth diagnostics framework for dealing with persistent economic growth failures:

Hausmann et al. - Doing Growth Diagnostics in Practice A Mindbook
Hausmann et al. – Doing Growth Diagnostics in Practice A Mindbook

With this decision tree:

Hausmann et al. - Doing Growth Diagnostics in Practice A Mindbook
Hausmann et al. – Doing Growth Diagnostics in Practice A Mindbook

The symptoms in the table indicate binding constraints. According to Hausmann and Rodrik, easing binding constraints would accelerate economic growth. Therefore, government should address its country’s constraints first. But before it must find these constraints.

Such evidence-based prioritizing could balance politics and fashion as the major determinants of public policies. It’s worth remembering that the major cost of government is not public spending, but the time wasted on implementing wrong reforms. This cost grows exponentially when measured against the scenario in which evidence-based policies indeed change things for better.

To be sure, governmental decisions are always accompanied by some sort of research. This research, however, often suffers from biases and politics. An economist needs a framework that keeps him disciplined. Hausmann-Rodrik growth diagnostics is such a framework.

A crash course in this framework would look like this:

  1. Hausmann, Klinger, and Wagner, “Doing Growth Diagnostics in Practice.”
  2. Hausmann, Rodrik, and Velasco, “Growth Diagnostics,” in Serra and Stiglitz, The Washington Consensus Reconsidered.
  3. World Bank, Country case studies.

I’d add two things to these materials.

First, a formal growth model. Doing diagnostics without modeling may seem easier, but after a while you’ll lose the big picture. As you lose the big picture, you can no longer rank priorities, even with microeconomic estimates of returns to policies. The Solow model and its modifications would suffice.

Also, unlike the authors, I’m more cautious about focusing on a single constraint. First, economic evidences are often inconclusive (but better than non-economic non-evidences). Second, governments fail to implement at least some of the policies that they’ve planned. In the end, you may advocate a wrong policy that isn’t going to be implemented anyway! As a remedy, I recommend stylized diversification akin to the Kelly criterion, when government allocates efforts proportionally to the expected payoffs from each constraint-policy pair.

In the next posts, I’ll review a couple of major economies in the spirit of Hausmann and Rodrik. Stay tuned!

How Big Data Informs Economics

In A Fistful of Dollars, Clint Eastwood challenges Gian Maria Volonte with the words, “When a man with .45 meets a man with a rifle, you said, the man with a pistol’s a dead man. Let’s see if that’s true. Go ahead, load up and shoot.”

That’s the right words to challenge big data, which recently reappeared in economics debates (Noah Smith, Chris House via Mark Thoma). Big data is a rifle, but not necessary winning. Economists must have special reasons to abandon small datasets and start messing with more numbers.

Unlike business, which only recently discovered the sexiest job of the future, economists do analytics for the last 150 years. They deal with “big data” for half of that period (I count from 1940, when the CPS started). So, how can the new big data be useful to them?

Let’s find out what big data offers. First of all, more information, of course. Notable cases include predicting the present with Google and Joshua Blumenstock’s use of mobile phones in development economics. Less notable cases encounter the same problem: a decline in the quality of data. Compare long surveys that development economists collect when they do experiments versus what Facebook dares to ask its most loyal users. Despite Facebook having 1.5 bn. observations, economists end up with much better evidences. That’s not about depth alone. Social scientists ask clearer questions, find representative respondents, and take nonresponses seriously. If you do a responsible job, you have to construct smaller but better samples like this.

Second, big data comes with its own tools, which, like econometrics, are deeply rooted in statistics but ignorant about causation:

Big data tools
Big data tools

The slogan is: to predict and to classify. But economics does care about cause and effect relations. Data scientists dispense with these relations because the professional penalty for misidentification is lower than in economics. And, honestly, at this stage, they have more important problems to solve. For example, much time still goes into capacity building and data wrangling.

Hal Varian shows a few compelling technical examples in his 2014 paper. One example comes from Kaggle’s Titanic competition:

Varian - 2014 - Big Data New Tricks for Econometrics
Varian – 2014 – Big Data New Tricks for Econometrics

The task requires predicting whether a person survived the crash or not. The chart says that children had more chances to survive than old passengers, while for the rest age didn’t matter. A regression tree captures this nonlinearity in the age, while logit regression does not. Hence, the big data tool does better than the economics tool.

But an economist who remembers to “always plot the data” is ready for this. Like with other big data tools, it’s useful to know the trees, but something similar is already available on the econometrics workbench.

There’s nothing ideological in these comments on big data. More data potentially available for research is better than less data. And data scientists do things economists can’t. The objection is the following. Economists mostly deal with the problems of two types. Type One, figuring out how n big variables, like inflation and unemployment, interact with each other. Type Two, making practical policy recommendations for the people who typically read nothing more than executive summaries. While big data can inform top-notch economics research, these two problems are easier to solve with simple models and small data. So, a pistol turns out to be better than a rifle.

Government Resistance to Human Capital

James Heckman has a great paper called “Policies to foster human capital“. Apart from excellent integration of economics into government policies, this paper relentlessly reminds the reader about the lifetime aspects of investments in human capital:

Heckman and Carneiro - 2003 - Human Capital Policy
Heckman and Carneiro – 2003 – Human Capital Policy

Heckman refers to the evidences that late investments do not pay off. Late learning costs money, including foregone earnings, but it only modestly increases wage rates and generates less human capital, since investments are made closer to retirement.

Heckman also criticizes the excessive attention to formal education, which is all about a few narrow cognitive abilities and in any case just one of many ways to acquire skills.

This paper was published in 2000. To count how many of its insights made it into government policies, I looked into the 2013 Economic Report of the President. This report includes a chapter on human capital.

The chapter discusses three things. First, labor inputs of women and immigrants. Second, rising debt and enrollment bias in college education. Third, educational opportunities for adults. So, formal education and adult learning?

Why does this influential publication promote the exact policies that Heckman criticized fifteen years ago? Alan Krueger — who edited the 2013 report — is a brilliant labor economist himself. The entire Council of Economic Advisors is well staffed. So it’s not about competence.

The problem seems to be in the complexity of the policies that Heckman proposes. When economists remind the Congress about student debt, they have better chances of being heard than economists who suggest targeted programs in preschool education. Student debt is something that even US presidents had. Preschool education? TL;DR.

But rephrasing Neil deGrasse Tyson, science is true whether you read it or not. The complex stuff still explains well why policies fail when consensus is reached.

For the United States, this complex stuff is about fine-tuning the system that works well. For the middle-income countries, on the other hand, ignorance is costlier, not least because these countries tend to aggravate mistakes with more government intervention. Policy makers in these countries often frame human capital accumulation as another industrialization. Like, if we could raise savings-investments to 50% of GDP, we can accumulate human capital in the same way. Obviously, it doesn’t work this way; and for BRICS, it’s an important challenge.

As for the least developed countries, the major innovators out there are international organizations, which recently got an open letter from Chris Blattman asking to “stop hurting” poor people with skill training programs. For the same reason: the programs don’t work as intended; even if being accumulated, this human capital solves no important problem.

“Stop hurting” is a good suggestion because it encourages policy makers to do less, which is the idea they like. The second part is more difficult: stop hurting and accept better policies. Some of the better policies look unconventional but remain as simple as training programs. Their main disadvantage is that they are not invented here, that is, in government. And before anything happens, someone has to market these policies as if they were government’s genuine invention.

Impact and Implementation of Evidence Based Policies

Chris Blattman noted that economists lack evidences on important policies. That’s true for foreign aid programs, which Chris mentioned. But defined broadly, policy making in poor countries can source evidences from elsewhere. NBER alone supplies 20 policy-relevant papers each week. And so does the World Bank, which recently studied its own economy:

About 49 percent of the World Bank’s policy reports … have the stated objective of informing the public debate or influencing the development community. … About 13 percent of policy reports were downloaded at least 250 times while more than 31 percent of policy reports are never downloaded. Almost 87 percent of policy reports were never cited.

In an ideal world, policy makers would read more and adjust their economies to the models we already know thanks to the decades of thorough research. This is not happening because policy makers are managers, not researchers with well-defined problems. And, as Russell Ackoff said, managers do not solve problems they manage messes.

Governments have their own limits of the messes they can deal with. Economists in research, on the contrary, simplify messes to tractable models. Let’s take one of the most powerful ideas in development: structural changes. Illustrated by Dani Rodrik:

McMillan and Rodrik - 2011 - Globalization, Structural Change and Productivity Growth
McMillan and Rodrik – 2011 – Globalization, Structural Change and Productivity Growth

The negative slope of the fitted values says that people moved from more productive to less productive industries over time. Which, of course, is a bad structural change. We can blame politics for this or whatever, but it’s hard to separate politics and, say, incompetence.

Emerging (and not so emerging) economies love the idea of employment growing in productive sectors. Even reports on sub-Saharan Africa regularly refer to knowledge economies and high-value-added industries. But in the end, many nations have something like that picture. (Oh, those messes.)

Did economists learn to manage messes better than public officials? Well, that’s what development economics is trying to accomplish. While it doesn’t include “general equilibrium effects” (the key takeaway from Daron Acemoglu), the baseline for judging the effectiveness of assessment programs is way below this and other criticisms. The baseline is eventually the intuition of a local public official—and policies that he would otherwise enact, if there were no evidence based programs.

Instead, these assessment programs provide simple tools for clear objectives. NGOs and local governments can expect something specific.

What about big evidence based policies? They require capacity building. At the extreme, look at the healthcare reform in the United States. Before anything happened, the Affordable Care Act already contained 1,000 pages. Implementation was difficult. Could a government in Central Africa implement a comparable reform, even having abundant evidences on healthcare in the US or at home?

Economists start to ignore the problem of implementation as the potential impact of their insights increases. The connection is not direct, but if you simplify a complex problem, you get a solution for the simplified problem. Someone else must complete the solution, and that becomes the problem.

Marketing by Elon Musk

While the Uber story shows that a poorly regulated industry may be a good place to start a new company, Elon Musk suggests another opportunity borne by government:

But government is inherently inefficient. So it makes sense to minimize the role of government such that government does only what it has to do, and no more.

After this quote, some people cut their Social Security cards into pieces and run to a libertarian sea platform, away from government. This is, however, not what Musk means. Here’s some background.

It’s not a secret that, since 1958, NASA received $1 trillion dollars from federal budget to create the stack of technologies that SpaceX currently uses in its own commercial projects. SpaceX’s initial capital of $100 million makes 0.01% of this investment in space odysseys. The other 99.99% came from the government, which is presumably the necessary minimum mentioned by Musk.

And as Musk rightly reminds in the same interview:

But funded by the government just means funded by the people. Government, by the way, has no money. It only takes money from the people. [Laughter.]

So SpaceX took away dozens of engineers trained by publicly funded NASA and secured at least $500 million in government contracts.

Tesla Motors, another company founded by Musk, sells cars eligible for a $7,500-worth federal subsidy and numerous of state subsidies of a comparable amount. It’s about 20% off each car to help Tesla compete with fossil fuel vehicles.

His third company, Solar City, also advertises solar tax credits and rebates as its competitive advantage over traditional utilities. It promises that “some [state governments] are generous enough to cover up to 30% of your solar power system cost.”

The subsidies are, of course, not the point here. They are the second way toward clean and renewable energy, after complete pricing of fossil fuels (which is broadly supported by economists, see Pigou Club). In practice this transition will happen very much like what Tesla and Solar City do now.

But for anyone practically or intellectually interested in how this business works, executives happen to be a pretty misleading source. Even when these executives write long books about their companies or hire well-known economists without giving them complete data. Instead of the story how the company really works, the reader gets ideological cliches about business, management, and government. With teachers like them, it’s not a surprise that 9 out of 10 startups end up nowhere.

This happens mostly in hi-tech, with all this sudden success and media exposure. But the most competent executives manage to keep a low profile even here, because they know that they are best at running companies, not at teaching people how things work.

The United States of Europe

While national parliaments in Europe are voting for the new Greek bailout, here’s an excellent counterfactual showing where Europe could be now if it had a real union:


That’s the summary of the choices that Europe made (and continues making) since at least 2000. If the year of 2009 explains itself with toxic assets and international shocks, the second dip is a European invention. It started with politicians denying the intention to bailout the indebted EU members. This ambiguity provoked the sovereign debt crisis in 2011-2012. The debt crisis, in turn, increased the cost of borrowing for the European countries that had to counteract high unemployment. Unable to do so, the periphery retained its 20-plus percent unemployment and the loss of output that followed.

To that the European Union responded in slow motion. The European Financial Stability Facility — the major vehicle of intra-EU bailouts created in 2010 — had been expanding constantly in response to the deteriorating situation in indebted economies. But actions took place only after the bad things had happened.

Now take the United States, which settled its major debt issues via the TARP in 2008-2009. More importantly, it was the federal government that paid $700 bn for this program. The American states didn’t waste time figuring out which state government was unacceptably immoral and, therefore, should have been reformed. In a hypothetical scenario, they could. The poorest American states have only a half of output per capita produced by the richest states. Also, the recovery was uneven:


Secondly, the US had the Fed that provided liquidity regardless of the bank’s state of origin. Meanwhile, as the first figure reminds, the ECB-led eurozone performed worse than the EU on average (and much worse compared to the nine EU countries that retained their national currencies).

This problem is much bigger than the Greek case alone. With Greece, European politicians ignore the most respected macroeconomic experts making reasonable arguments. But these politicians are not supposed to listen to the reason, if by reason we understand the wellbeing of an average European. Merkel and Schauble are accountable to the German voter, not to the Greeks. And the German voter is fine with paying nothing to Greece. He can continue enjoying low unemployment at home — even if this became possible thanks to the euro weakened by the indebted EU members.


Paul Krugman shows the divergence between Sweden and Finland. Finland is a eurozone member lagging behind Sweden, with an overall picture similar to the first figure here.

The French Connection

One month ago the French police arrested two Uber executives for running an illegal cab company (yes, Uber) — the sort of accusations supported by the French court. I hope the CEO of Uber won’t end like Al Capone, but I would say a couple of good things about the company in advance.

Uber found an ironclad source of value: a heavily taxed and regulated industry with unsophisticated laws. A few changes in the business model totally confused regulators, and Uber currently enjoys a tax advantage across North America and Western Europe. The company surely shares the profits from this advantage with its drivers and clients, but at the expense of other cab services.

These traditional cab services operate in a boring market where no one makes big profits. High prices usually just include all the payments to the city, like expensive licenses and employee-related taxes. Are these payments a waste? In cities like Paris tourists enjoy clean streets and good roads because they pay this high price for personal transport. And so do locals: a taxi means more jams, more pollution, and more roads. On the contrary, when cities keep transport-related taxes low, the mayor gets reelected but the entire city spend each morning in jams.

What about the better drivers that Uber has? Actually, they are paid higher wages:


How’s that? The part-time employment that implies lower taxes (and cost hiding). Which brings us back to the argument above.

Yep, Uber has all those driver ratings and such, but even small traditional taxi companies learned how to get feedback on their drivers. But better employees want wages that are — taxes included — incompatible with the industry.

The second success factor of Uber-like multinational taxi services is the McDonald’s signal. Wherever you happen to be, there’s a company with the known standards of quality. As for taxis, at least you won’t end up in the wrong part of the city with the driver having his first week in a new country. But that’s for folks who travel a lot across cities, so not the biggest deal.

It’s possible, of course, that Uber creates value in other ways, like managing its cab fleet better. They don’t reveal this information. They did reveal the interest in replacing humans with self-driving cars after their raid on Carnegie Mellon, but that’s for the future.

This sounds less revolutionary and disruptive than the “sharing economy” evangelism, but startup founders waste time on ideological companies that fail because sharing by itself creates little value. It’s really better to spend less time in development and more time in looking for real sources of value here.

What If Germany Annexed Greece?

The obsession with Greek debt shadowed the whole point of borrowing—that is, helping Greece grow again. While the debt matters, it matters even more when it’s well spent on policies that would reverse the recession. And this genre is totally different from today’s media coverage.

What plans does Europe have for Greece?

Greece itself doesn’t stick to any long-term plan. That’s both good news and bad news. Having several years of economic decline in a row, people try to rotate the left and the right in power, and new elections introduce new policies. The Papandreou government prioritized the business climate. In the best-practice style, it committed itself to the 2012 bailout terms and Greece even earned 48 positions in the World Bank’s Doing Business ranking since 2010 (praise by St. Louis Fed’s economist). This is how GDP responded:


Getting rid of bureaucracy solved no urgent economic problem. Business can include red tape in the price, but it can’t escape the falling demand on its own. Eventually, Greece got a good business climate without business.

Syriza had tried to argue with the creditors, so in response, the ECB cut the Greek banks off liquidity. Surely, the ECB did so in compliance with the June 30 official deadline, but it could negotiate the extension. Such events shake the public opinion, and Greek governments change each other. So which plan of long-term growth should we assess?

The plan of the creditors, of course. They have a large impact on short-term economic stability, which is a lever for making long-term decisions. With a small abuse of democracy, the troika writes a detailed plan for Greece and waits till the Greek officials accept this plan as their own.

This plan escapes the debtor’s fluctuating politics, so the bottom line remains the same since 2012. It includes the “milestones” that unlock new tranches from the EU:


The full manual concerns just everything:


(Along with some alternatives by academics, this plan tells something about the contribution of economic growth theory to the current affairs, or, rather, the lack of thereof. While growth theory speaks esoteric ideas, like creative destruction and rule of law, practitioners reduce recommendations to straightforward cost-cutting.)

But isn’t there a conflict of interest in this plan? A creditor’s planning horizon ends where his bonds mature. The debtor hopefully lives a little longer. So, their plans don’t coincide. If the troika wants its interest from the Greek bonds (that mature in 5−10 years), then its plan may be short-termed, compared to the recovery cycle. For example, the US stimulus was opting out of the economy for six years:


Maybe Angela Merkel loves the Greek people as much as the German people who elected her. But if not, which system would provide a German plan consistent with the Greek interests? Perhaps, the one where the Greek and German people elect a single representative.

How would it happen if merely adopting a single European currency took fifty years? Through the crises like the current one. The troika pays Greece for being more like Germany. As the new agreement is getting closer, this crisis management team continues controlling Greece even after the ruling party has been replaced. Then, wouldn’t the Greek people have more power if they affected the German politics directly, in joint elections?

That would be possible if the Greeks couldn’t quit. But Germany can’t take over its neighbors like the Thirteen Colonies did. And there’s already much disagreement about voluntary unification:


Greece, Spain, and Italy don’t enjoy being a hostage of the euro, even if this allows them to borrow cheap. Whatever these countries get from being more like Germany, they get it after giving up independence. How much could they get? The local unifications of Italy and of Germany didn’t lead to the full convergence of the regions, so the prospects are unclear. Meanwhile, it’s not even clear what “being more like Germany” means because the north of Germany is poorer than some South European regions:

Income per capita in European regions (FT)

If the Greek people could affect German politics, this would lead to more consistent plans, but not necessarily effective. Again, we know some general things about taming GDP fluctuations, but too little about the impact of structural reforms. And after the troika switched from macro to structural reforms, it must explain well why their experts think they know Greece better than the Greeks do.

The Box of Economic Growth Theory

Authors preaching creativity often use the nine-dots puzzle to encourage the reader to think, well, wider.

So, I did the experiment on the favorite topic. In economic growth, the box is a country. And researchers try to get most out of its economy, either by flattening the economic cycle or changing the long-term growth rates. While macroeconomists learned powerful tools for managing the cycles, the long-term rates remain untamed. Several competing frameworks describe the determinants of the rates, but not the determinants of the determinants. Which leaves you confused.

But you can think outside the box! Outside the box, you have other countries. A few of them have higher GDP per capita, so instead of thinking how to make people inside Country A richer, you can think how to relocate people from poor countries to the rich ones. The trick is, productivity grows when a person moves to a more industrious place, and so does output.

Urbanization is an old example of relocation at work, still relevant for many developing countries. But urbanization makes a small fraction of global income differences, which generally look like this:

Milanovic - 2011 - Global Inequality
Milanovic – 2011 – Global Inequality

Growth theory is figuring out how to change the shape and placements of these curves. This is difficult. But a single person can move along his country’s plot, or he can jump into another country’s plot. While jumping, the person boosts his productivity and the world GDP increases, despite both countries retain their GDP per capita levels.

As Branko Milanovic says in the paper where this chart comes from, migration is “probably the most powerful tool for reducing global poverty.” It’s not only powerful, it’s simple, compared to other solutions.

Now, what’s wrong with it. Like any solution, migration needs devoted advocates. It’s easy to find advocates for sound macro policies. Citizens don’t want to spend years in recession, so they wage research and lobbying. But advocating immigration reforms is like a part-time job, because citizens don’t care about the foreigner’s income. For example, some think tanks like the idea of letting foreign doctors and lawyers in the US, since that would reduce domestic inequality created by premium wages in these sectors. (No other country has 14 healthcare professionals in the top 20 of highest paying occupations.)

There’re, of course, bargains with undocumented immigrants in exchange for political support, like the great pardon proposed by Obama. This is a poor replacement for a legal-immigration reform, which would be more beneficial for the US at large.

What works? Developed countries invite immigrants to fill the gaps in the shrinking population. The current generation seek someone who will support them in the future. This looks like a perpetual immigration engine: when you have no children and retire, you just “adopt” an immigrant in his 20s who’ll pay the taxes that fund public spending. Then this immigrant gets older and lets a younger one to come in, and so on. Since the real birthrates are never zero, few workers change countries this way.

Business lobby promotes another channel, the expansionary one. When Bill Gates writes that the US needs immigrants to remain competitive, he means that Indian software firms are threatening Microsoft. Giving Indian software engineers US visas deprives Indian firms of skilled labor, so both India and Indian firms no longer compete with American software developers. That works well for both US business and citizens and, therefore, is a viable solution.

However, these channels create opportunities for a small fraction of internationally attractive professionals. The others remain dependent on domestic growth. The international response? Investments, not visas. That’s getting us back into the box, because investments depend on the country’s growth rates. The investor won’t come for the same reasons why the country doesn’t manage its currently available resources well. And despite the problem of growth has reasonable outside-the-box solutions, domestic politics accepts only inside-the-box options.

Maybe that’s why thinking in the box is still very useful in economics.

Markets and Behavioral Economics

In recent years, the mentions of “behavioral economics” in the books reached 1/3 of the mentions of The Simpsons, which is a big success of science. As usual, this research revolves around cognitive biases. But how far do these biases go? I’d say, biases end where markets start.

While most economic theories try to be general (often unsuccessfully so), behavioral economics suffers from the opposite. Experiments with college students—which the field was producing over the years—describe how humans think when they are in their early 20s filling questionnaire right before the lecture. These results don’t describe an experienced stock broker making million-worth transactions each day for the last 10 years.

Behavioral economics loses its explanatory power as bets go up. Nudging works in ecommerce, in everyday services. But as money and competition appear, irrationality vanishes. And it happens way before multimillion deals.

One landmark finding by Kahneman was the weighting function in his prospect theory:

Kahneman and Tversky - 1979 - Prospect Theory
Kahneman and Tversky – 1979 – Prospect Theory

It says that people overestimate the probability of unlikely events. Kahneman and Tversky derived this from the experiments with their undergrads, and you see how the solid line deviates from the “correct” dotted line by a few percentage points.

These few points get the magnitude in big decisions. A high school grad who wants to be a movie star plays against the odds. Movie appearances are skewed toward few superstars, and the probability of being one of them has many zeros after the decimal point. How does the grad percept his chances? He misses a few zeros, thus taking success as more probable. The person makes choices he wouldn’t make if he knew the true probabilities.

But in money markets, humans learn probabilities faster. The market does start with misestimated probabilities. For example, profitable sports betting strategies make money out of the people who bet on underdogs:

Hausch and Ziemba - 2008 - Handbook of sports and lottery markets
Hausch and Ziemba – 2008 – Handbook of sports and lottery markets

The bets don’t break even because the winners pay the house, too. This strategy yields positive returns in other popular sports, like soccer. But professional bettors leave these markets as big bets behave rationally and kill successful strategies. The bettors choose unknown sports, as jai alai. Or they bet on the goal difference, or particular players. All in attempts to find small markets, where irrationality resides.

Financial markets dwarf sports betting. How’s likely any human bias then? Not at all. Money managers with biases leave the table quickly. The markets have inefficiencies, but for other reasons and with other implications. A famous example of statistical arbitrage by LTCM:

Long-Term bought the cheaper off-the-run bond, while simultaneously selling the more expensive on-the-run bond. This allowed it to lock in the spread between the two bonds while immunizing it from interest rate movements. LTCM didn’t want to bet on the future of interest rates, instead, it wanted to make a very specific bet on liquidity. By creating a long position in one bond and a short position in another similar bond, LTCM knew that any losses from interest rate movements in one bond would be wiped out by equivalent gains in the other bond.

One problem with this trade, however, was that the spread between the two types of treasuries tended to be very small. For example, in August 1993, before Long-Term entered the market, 30-year bonds yielded 7.24%, while 29½ year bonds yielded 7.36%. This 12 basis point spread would not allow it to earn the type of returns that its investors expected, so the traders at LTCM needed to leverage their trade in order to magnify this return.

Hoping that the yields converge, LTCM bought positions in bonds with maturity 30 and 29½ years. Then the 1997 Asian crisis and 1998 Russia’s default occurred, and investors fled to quality. Investors were buying 30-year bonds, and not the 29½. As 30-year bonds grew in price, their yield declined and the gap between the two types of bonds increased. LTCM suffered immediate losses that wiped out the profits from the previous four years.

LTCM was an unusual hedge fund. Its founders were careful academic researchers with solid models. So, they found a good balance between profitability and bold assumptions about the markets. The result? The 12 basis point spread is a sort of inefficiency that big markets offer, and hedge funds can’t take it free of risks. The spread had nothing to do with human rationality. Buying 29½ bonds just happened to be a different market.

In between college questionnaires and Treasury bonds, where do cognitive biases cease to be a good approximation of reality? Behavioral economics can’t say. Social sciences don’t come up with universal models; they only show how to do certain thing better. Here, psychologists showed how to do elegant experiments that predict the future within a specific domain. So far, these experiments have been successfully adopted by UX designers and advertising. Whatever one thinks about their ethics, ads now waste less than in the Mad Men period—exactly because pros became as disciplined as earlier scientists. Meanwhile, governments—which could learn a lot from behavioral economics—adopt these things sluggishly, as recent results by the UK nudge unit show. But that’s also connected with markets and competition.