Key Points from A World Without Work

The world is rapidly changing and many tasks that has historically deemed to be impossible to be automate are now either done completely by robots, or aided by it. With technological advancement only to accelerate, what is the value of human work and where is the trend heading? In his great and mind-blowing book, Daniel Susskind provides a clue on the answers. The key points below are an excerpt from the book and serves as a personal note to me and my friends.


It was John Maynard Keynes, the great British economist, who popularized the term “technological unemployment” almost fifty years before Leontief wrote down his worries, capturing in a pithy pairing of words the idea that new technologies might push people out of work. In what follows, I will draw on many of the economic arguments that have been developed since Keynes to try to gain a better look back at what happened in the past, and a clearer glimpse of what lies ahead. But I will also seek to go well beyond the narrow intellectual terrain inhabited by most economists working in this field. The future of work raises exciting and troubling questions that often have little to do with economics: questions about the nature of intelligence, about inequality and why it matters, about the political power of large technology companies, about what it means to live a meaningful life, about how we might live together in a world that looks very different from the one in which we have grown up. In my view, any story about the future of work that fails to engage with these questions as well is incomplete.

Even at the century’s end, tasks are likely to remain that are either hard to automate, unprofitable to automate, or possible and profitable to automate but which we will still prefer people to do.

Machines will not do everything in the future, but they will do more. And as they slowly, but relentlessly, take on more and more tasks, human beings will be forced to retreat to an ever-shrinking set of activities. It is unlikely that every person will be able to do what remains to be done; and there is no reason to imagine there will be enough demand for it to employ all those who are indeed able to do it.

A useful way of thinking about what this means is to consider the impact that automation has had already had on farming and manufacturing in many parts of the world. Farmers and factory workers are still needed: those jobs have not completely vanished. But the number of workers that is needed has fallen in both cases, sometimes precipitously—even though these sectors produce more output than ever before.

But it is still helpful in highlighting what should actually be worrying us about the future: not a world without any work at all, as some predict, but a world without enough work for everyone to do.

It is not a coincidence that, today, worries about economic inequality are intensifying at the exact same time that anxiety about automation is growing. These two problems—inequality and technological unemployment—are very closely related. Today, the labor market is the main way that we share out economic prosperity in society: most people’s jobs are their main, if not their only, source of income. The vast inequalities we already see in the labor market, with some workers receiving far less for their efforts than others, show that this approach is already creaking.

Technological unemployment, in a strange way, will be a symptom of that success. In the twenty-first century, technological progress will solve one problem, the question of how to make the pie large enough for everyone to live on. But, as we have seen, it will replace it with three others: the problems of inequality, power, and purpose.

From the outset, it seems, economic growth and automation anxiety were intertwined.

Yes, people did tend to find new work after being displaced by technology—but the way in which this happened was far from being gentle or benign. Take the Industrial Revolution again, that textbook moment of technological progress. Despite the Luddites’ fears, the unemployment rate in Britain remained relatively low, as we can see in Figure 1.2. But, at the same time, whole industries were decimated, with lucrative crafts like hand weaving and candle making turned into profitless pastimes. Communities were hollowed out and entire cities thrust into decline. It is noteworthy that real wages in Britain barely rose—a measly 4 percent rise in total from 1760 to 1820. Meanwhile food became more expensive, diets were poorer, infant mortality worsened, and life expectancy fell.21 People were, quite literally, diminished: a historian reports that average physical heights fell to their “lowest ever levels” on account of this hardship.22

Take the Industrial Revolution again, that textbook moment of technological progress. Despite the Luddites’ fears, the unemployment rate in Britain remained relatively low, as we can see in Figure 1.2. But, at the same time, whole industries were decimated, with lucrative crafts like hand weaving and candle making turned into profitless pastimes. Communities were hollowed out and entire cities thrust into decline. It is noteworthy that real wages in Britain barely rose—a measly 4 percent rise in total from 1760 to 1820. Meanwhile food became more expensive, diets were poorer, infant mortality worsened, and life expectancy fell.21

In the OECD—the Organisation for Economic Cooperation and Development, a club of several dozen rich countries—the average number of hours that people work each year has continuously fallen over the past fifty years. The decline has been slow, about forty-five hours a decade, but steady nonetheless.

Importantly, a large part of this decline appears to be associated with technological progress and the increases in productivity that came along with it. Germany, for instance, is among the most productive countries in Europe, and also the one where people work the fewest hours a year. Greece is among the least productive, and—contrary to what many might think—the one where people work the most hours a year.

Technological change may affect not only the amount of work, but also the nature of that work. How well-paid is the work? How secure is it? How long is the working day, or the working week? What sort of tasks does the work involve—is it the fulfilling sort of activity you leap out of bed in the morning to do, or the sort that keeps you hiding under the covers? The risk, in focusing on jobs alone, is not so much failing to see the proverbial forest for the trees, but failing to see all the different trees in the forest.

Yes, machines took the place of human beings in performing certain tasks. But machines didn’t just substitute for people; they also complemented them at other tasks that had not been automated, raising the demand for people to do that work instead. Throughout history, there have always been two distinct forces at play: the substituting force, which harmed workers, but also the helpful complementing force, which did the opposite.

new technologies may automate some tasks, taking them out the hands of workers, but make those same workers more productive at the tasks that remain for them to do in their jobs.

In all these cases, if productivity increases are passed on to customers via lower prices or better-quality services, then the demand for whatever goods and services are being provided is likely to rise, and the demand for human workers along with it. Through the productivity effect, then, technological progress complements human beings in a very direct way, increasing the demand for their efforts by making them better at the work that they do.

if productivity increases are passed on to customers via lower prices or better-quality services, then the demand for whatever goods and services are being provided is likely to rise, and the demand for human workers along with it. Through the productivity effect, then, technological progress complements human beings in a very direct way, increasing the demand for their efforts by making them better at the work that they do.

technological progress has made the pie far bigger. As previously noted, over the last few hundred years, economic output has soared.

Intuitively, growth like this is likely to have helped workers. As an economy grows, and people become more prosperous with healthier incomes to spend, the opportunities for work are likely to improve. Yes, some tasks might be automated and lost to machines. But as the economy expands, and demand for goods and services rises along with it, demand will also increase for all the tasks that are needed to produce them. These may include activities that have not yet been automated, and so displaced workers can find work involving them instead.

As an economy grows, and people become more prosperous with healthier incomes to spend, the opportunities for work are likely to improve. Yes, some tasks might be automated and lost to machines. But as the economy expands, and demand for goods and services rises along with it, demand will also increase for all the tasks that are needed to produce them. These may include activities that have not yet been automated, and so displaced workers can find work involving them instead.

Kenneth Arrow, a Nobel Prize–winning economist, likewise argued that historically, “the replacement of men by machines” has not increased unemployment. “The economy does find other jobs for workers. When wealth is created, people spend their money on something.”

If we think again of the economy as a pie, new technologies have not only made the pie bigger, but changed the pie, too. Take the British economy, for example. Its output, as we noted, is now more than a hundred times what it was three centuries ago. But that output, and the way it is produced, has also completely transformed. Five hundred years ago, the economy was largely made up of farms; three hundred years ago, of factories; today, of offices.

Again, it is intuitive to see how these changes might have helped displaced workers. At a certain moment, some tasks might be automated and lost to machines. But as the economy changes over time, demand will rise for other tasks elsewhere in the economy. And since some of these newly in-demand activities may, again, not have been automated, workers can find jobs involving them instead. To see this changing-pie effect in action, think about the United States. Here you can see displaced workers tumbling through a changing economy, time and again, into different industries and onto different tasks. A century ago, agriculture was a critical part of the American economy: back in 1900, it employed two in every five workers. But since then, agriculture has collapsed in importance and today it employs fewer than two in every hundred workers.37 Where did the rest of those workers go? Into manufacturing. Fifty years ago, that sector superseded agriculture: in fact, in 1970, manufacturing employed a quarter of all American workers. But then that sector also went into relative decline and today fewer than a tenth of American workers are employed in it.38 Where did these displaced factory workers go? The answer is the service sector, which now employs more than eight in ten workers.39 And there is nothing distinctly American about this story of economic transformation, either. Almost all developed economies have followed a similar path, and many less-developed economies are following it, too.40 In 1962, 82 percent of Chinese workers were employed in agriculture; today, that has fallen to around 31 percent, a larger and faster decline than the American one.41

puzzle was that in the twentieth century, there were prolonged periods where the reverse appeared to happen in the world of work. In some countries, there was huge growth in the number of high-skilled people pouring out of colleges and universities, yet their wages appeared to rise rather than fall compared to those without this education. How could this be? The skill-biased story provided an answer. The supply of high-skilled workers did grow, pushing their wages downward, but new technologies were skill-biased and so caused the demand for high-skilled workers to soar. The latter effect was so great that it overcame the former, so even though there were more educated people looking for work, the demand for them was so strong that the amount they were paid still went up.

Another way to see the skill-biased story at work is to look at how wages have changed over time for a variety of different levels of schooling. This is shown in Figure 2.3. As the charts show, people with more years of schooling not only tend to earn more at every point in the past half century, but the gap between them and those with less schooling has tended to grow over time as well. (For women, this story becomes clearer from the 1980s onward.)

This longer view suggests that technological change has in fact favored different types of workers at different moments in history, not always benefiting those who might have been considered skilled at that particular time. Take the nineteenth century, for example. As we saw in the previous chapter, when the Industrial Revolution got under way in Britain, new machines were introduced to the workplace, new production processes were set up, and so new tasks had to be done. But it turned out that those without the skills of the day were often best placed to perform these tasks. Technology, rather than being skill-biased, was “unskill-biased” instead.

These new machines were “de-skilling,” making it easier for less-skilled people to produce high-quality wares that would have required skilled workers in the past.

At the turn of the twenty-first century, then, the conventional wisdom among economists was that technological progress was sometimes skill-biased, at other times unskill-biased. In either case, though, many economists tended to imagine that this progress always broadly benefited workers. Indeed, in the dominant model used in the field, it was impossible for new technologies to make either skilled or unskilled workers worse off; technological progress always raised everyone’s wages, though at a given time some more than others. This story was so widely told that leading economists referred to it as the “canonical model.”

Starting in the 1980s, new technologies appeared to help both low-skilled and high-skilled workers at the same time—but workers with middling skills did not appear to benefit at all. In many economies, if you took all the occupations and arranged them in a long line from the lowest-skilled to the highest-skilled, over the last few decades you would have often seen the pay and the share of jobs (as a proportion of total employment) grow for those at either end of the line, but wither for those near the middle.

In many economies, if you took all the occupations and arranged them in a long line from the lowest-skilled to the highest-skilled, over the last few decades you would have often seen the pay and the share of jobs (as a proportion of total employment) grow for those at either end of the line, but wither for those near the middle.

This phenomenon is known as “polarization” or “hollowing out.” The traditionally plump midriffs of many economies, which have provided middle-class people with well-paid jobs in the past, are disappearing. In many countries, as a share of overall employment there are now more high-paid professionals and managers—as well as more low-paid caregivers and cleaners, teacher’s aides and hospital assistants, janitors and gardeners, waiters and hairdressers.17 But there are fewer middling-pay secretaries and administrative clerks, production workers and salespeople.18 Labor markets are becoming increasingly two-tiered and divided.

With time, it became clear that the level of education required by human beings to perform a given task—how “skilled” those people were—was not always a helpful indication of whether a machine would find that same task easy or difficult. Instead, what appeared to matter was whether the task itself was what the economists called “routine.” By “routine,” they did not mean that the task was necessarily boring or dull. Rather, a task was regarded as “routine” if human beings found it straightforward to explain how they performed it—if it relied on what is known as “explicit” knowledge, knowledge which is easy to articulate, rather than “tacit” knowledge, which is not.23

That was why labor markets around the world were being hollowed out, taking on hourglass figures. Technological change was eating away at the “routine” tasks clustered in the middle, but the “non-routine” tasks at either end were indigestible, left for human beings to undertake.

Technological progress, it appeared, was neither skill-biased nor unskill-biased, as the old stories had implied. Rather it was task-biased, with machines able to perform certain types of tasks but unable to perform others. This meant that the only workers to benefit from technological change would be those well placed to perform the “non-routine” tasks that machines could not handle. In turn, this explained why certain types of middling-skilled workers might not gain from new technology at all—if they found themselves stuck in jobs made up largely of “routine” tasks that machines could handle with ease.

The point is driven home by a 2017 study carried out by McKinsey & Company, which reviewed 820 different occupations. Fewer than 5 percent of these, they found, could be completely automated with existing technologies. On the other hand, more than 60 percent of the occupations were made up of tasks of which at least 30 percent could be automated.30 In other words, very few jobs could be entirely done by machines, but most could have machines take over at least a significant part of them.

some of today’s greatest pragmatist triumphs have grown out of earlier purist attempts to copy human beings. For instance, many of the most capable machines today rely on what are known as “artificial neural networks,” which were first built decades ago in an attempt to simulate the workings of the human brain.27 Today, though, there is little sense that these networks should be judged according to how closely they imitate human anatomy; instead, they are evaluated entirely pragmatically, according to how well they perform whatever tasks they are set.

In sum, both the theologians and the AI scientists believed that remarkable capabilities could only ever emerge from something that resembled human intelligence. In the words of the philosopher Daniel Dennett, both thought that competence could only emerge from comprehension, that only an intelligent process could create exceptionally capable machines.43 Today, though, we know that the religious scholars were wrong. Humans and human capabilities were not created through the top-down efforts of something more intelligent than us, molding us to look like it. In 1859, Charles Darwin showed that the reverse was true: the creative force was a bottom-up process of unconscious design. Darwin called this “evolution by natural selection,” the simplest account of it only requiring you to accept three things: first, that there are slight variations between living beings; second, that some of these variations might be favorable for their survival; and third, that these variations are passed on to others. There was no need for an intelligent designer, directly shaping events; these three facts alone could explain all appearances of design in the natural world. The variations might be tiny, the advantages ever so slight, but these changes, negligible at any instant, would—if you left the world to run for long enough—accumulate over billions of years to create dazzling complexity. As Darwin put it, even the most “complex organs and instincts” were “perfected, not by means superior to, though analogous with, human reason, but by the accumulation of innumerable slight variations, each good for the individual possessor.”

The pragmatist revolution in AI requires us to make a similar reversal in how we think about where the abilities of man-made machines come from. Today, the most capable systems are not those that are designed in a top-down way by intelligent human beings. In fact, just as Darwin found a century before, remarkable capabilities can emerge gradually from blind, unthinking, bottom-up processes that do not resemble human intelligence at all.

The ancient Greek poet Archilochus once wrote: “The fox knows many things, but the hedgehog knows one big thing.” Isaiah Berlin, who found this mysterious line in the surviving scraps of Archilochus’s poetry, famously used it as a metaphor to distinguish between two types of human being: people who know a little about a lot (the foxes) and people who know a lot about a little (the hedgehogs).20 In our setting, we can repurpose that metaphor to think about human beings and machines. At the moment, machines are prototypical hedgehogs, each of them designed to be very strong at some extremely specific, narrowly defined task—think of Deep Blue and chess, or AlphaGo and go—but hopeless at performing a range of different tasks. Human beings, on the other hand, are proud foxes, who might now find themselves thrashed by machines at certain undertakings, but can still outperform them at a wide spread of others.

For many AI researchers, the intellectual holy grail is to build machines that are foxes rather than hedgehogs. In their terminology, they want to build an “artificial general intelligence” (AGI), with wide-ranging capabilities, rather than an “artificial narrow intelligence” (ANI), which can only handle very particular assignments.

In short, when thinking about the future of work, we should be wary not of one omnipotent fox, but an army of industrious hedgehogs.

Economists had thought that to accomplish a task, a computer had to follow explicit rules articulated by a human being—that machine capabilities had to begin with the top-down application of human intelligence. That may have been true in the first wave of AI. But as we have seen, it is no longer the case. Machines can now learn how to perform tasks themselves, deriving their own rules from the bottom up. It does not matter if human beings cannot readily explain how they drive a car or recognize a table; machines no longer need those human explanations. And that means they are able to take on many “non-routine” tasks that were once considered to be out of their reach.

The idea that such machines are uncovering hitherto hidden human rules, plunging deeper into people’s tacit understanding of the world, still supposes that it is human intelligence that underpins machine capability. But that misunderstands how second-wave AI systems operate. Of course, some machines may indeed stumble upon unarticulated human rules, thereby turning “non-routine” tasks into “routine” tasks. But far more significant is that many machines are also now deriving entirely new rules, unrelated to those that human beings follow. This is not a semantic quibble, but a serious shift. Machines are no longer riding on the coattails of human intelligence.

If machines do not need to copy human intelligence to be highly capable, the vast gaps in science’s current understanding of intelligence matter far less than is commonly supposed. We do not need to solve the mysteries of how the brain and mind operate to build machines that can outperform human beings. And if machines do not need to replicate human intelligence to be highly capable, there is no reason to think that what human beings are currently able to do represents a limit on what future machines might accomplish. Yet this is what is commonly supposed—that the intellectual prowess of human beings is as far as machines can ever reach.45 Quite simply, it is implausible in the extreme that this will be the case.

We can think of this general trend, where machines take on more and more tasks that were once performed by people, as “task encroachment.”9 And the best way to see it in action is to look at the three main capabilities that human beings draw on in their work: manual, cognitive, and affective capabilities. Today, each of these is under increasing pressure.

First, take the capabilities of human beings that involve dealing with the physical world, such as performing manual labor and responding to what we see around us. Traditionally, this physical and psychomotor aptitude was put to economic use in agriculture. But over the last few centuries, that sector has become increasingly automated.

That human alternative, not perfection, should be the benchmark for judging the usefulness of these diagnostic machines.

At times, the encroachment of machines on tasks that require cognitive capabilities in human beings can be controversial. Consider the military setting: there are now weapons that can select targets and destroy them without relying on human deliberation. This has triggered a set of United Nations meetings to discuss the rise of so-called “killer robots.”56 Or consider the unsettling field of “synthetic media,” which takes the notion of tweaking images with Photoshop to a whole new level. There are now systems that can generate believable videos of events that never happened—including explicit pornography that the participants never took part in, or inflammatory speeches by public figures that they never delivered. At a time when political life is increasingly contaminated by fake news, the prospects for the misuse of software like this are troubling.

There are systems that can outperform human beings in distinguishing between a genuine smile and one of social conformity, and in differentiating between a face showing real pain and fake pain. And there are also machines that can do more than just read our facial expressions. They can listen to a conversation between a woman and a child and determine whether they are related, and tell from the way a person walks into a room if they are about to do something nefarious.62 Another machine can tell whether a person is lying in court with about 90 percent accuracy—whereas human beings manage about 54 percent, only slightly better than what you might expect from a complete guess.63 Ping An, a Chinese insurance company, uses a system like this to tell whether loan applicants are being dishonest: people are recorded as they answer questions about their income and repayment intentions, and a computer evaluates the video to check whether they are telling the truth.

Economists often label tasks according to the particular capabilities that human beings use to perform them. They talk, for instance, about “manual tasks,” “cognitive tasks,” and “interpersonal tasks,” rather than about “tasks which require manual, cognitive, or interpersonal capabilities when performed by human beings.” But that way of thinking is likely to lead to an underestimation of quite how far machines can encroach in those areas. As we have seen time and again, machines can increasingly perform various tasks without trying to replicate the particular capabilities that human beings happen to use for them. Labeling tasks according to how humans do them encourages us to mistakenly think that machines could only do them in the same way.

The Rise and Fall of American Growth is magisterial and yet, in a sense, self-contradictory. It argues with great care that growth was “not a steady process” in the past, yet it concludes that a steady process is exactly what we face in the future: a steady process of decline in economic growth, with ever fewer unexpected innovative bursts and technological breakthroughs of the kind that drove our economies onward in the past. Given the scale of investment in technology industries today—many of our finest minds, operating in some of the most prosperous institutions—it seems entirely improbable that there will be no more comparable developments in years to come.

The general lesson here is that, in thinking about whether or not it is efficient to use a machine to automate a task, what matters is not only how productive that machine is relative to the human alternative, but also how expensive it is relative to the human alternative. If labor is very cheap in a particular place, it may not make economic sense to use a pricey machine, even if that machine turns out to be very productive indeed.

Perhaps the most interesting implications of relative costs are the international ones. In part, these cost variations between countries can explain why new technologies have been adopted so unevenly around the world in the past. A big puzzle in economic history, for instance, is why the Industrial Revolution was British, rather than, say, French or German. Robert Allen, an economic historian, thinks relative costs are responsible: at the time, the wages paid to British workers were much higher than elsewhere, while British energy prices were very low. Installing new machines that saved on labor and used readily available cheap fuel thus made economic sense in Britain, whereas it did not in other countries.

countries that are aging faster tend to invest more in automation. One study found that a 10 percent increase in the ratio of workers above fifty-six to those between twenty-six and fifty-five was associated with 0.9 more robots per thousand workers.

there is still work to be done by human beings: the problem is that not all workers are able to reach out and take it up.

“Frictions” in the labor market prevent workers from moving freely into whatever jobs might be available. (If we think of the economy as a big machine, it is as if there is sand or grit caught up in its wheels, stopping its smooth running.) Today, there are already places where this is happening. Take men of working age in the United States, for instance. Since World War II, their participation in the labor market has collapsed: one in six are now out of work, more than double the rate of 1940.4 What happened to them? The most compelling answer is that these men fell into frictional technological unemployment. In the past, many of them would have found well-paid work in the manufacturing sector. Yet technological progress means that this sector no longer provides sufficient work for them all to do: in 1950, manufacturing employed about one in three Americans, but today it employs fewer than one in ten.5 Plenty of new jobs have been created in other sectors as the US economy changed and grew—since 1950, it has expanded about fourfold—but critically, many of these displaced men were not able to take up that work. For a variety of reasons, it lay out of their reach.

human beings are likely to find this race with technology ever harder, because its pace is accelerating. Literacy and numeracy are no longer enough to keep up, as they were when workers first made the move from factories to offices at the turn of the twentieth century. Ever higher qualifications are required. Notably, while workers with a college degree have been outperforming those with only a high school education, those with postgraduate qualifications have seen their wages soar far more,

a third of Americans with degrees in STEM subjects (science, technology, engineering, and math) are now in roles that do not require those qualifications.18 And when economists took all the jobs performed by US college graduates and examined the tasks that make them up, they found a collapse in the “cognitive task intensity” of these roles from 2000 onward—a “great reversal in the demand for skills.”

There is a common fantasy that technological progress must make work more interesting—that machines will take on the unfulfilling, boring, dull tasks, leaving behind only meaningful things for people to do. They will free us up, it is often said, to “do what really makes us human.” (The thought is fossilized in the very language we use to talk about automation: the word robot comes from the Czech robota, meaning drudgery or toil.) But this is a misconception. We can already see that a lot of the tasks that technological progress has left for human beings to do today are the “non-routine” ones clustered in poorly paid roles at the bottom of the labor market, bearing little resemblance to the sorts of fulfilling activities that many imagined as being untouched by automation. There is no reason to think the future will be any different.

On the face of it, Americans appear to be remarkably mobile: about half of households change their address every five years, and the proportion of people living in a different state from the one where they were born has risen to one-third.32 But there are two important caveats. First, this is not the case everywhere. Europeans, for instance, are far more immovable: 88.3 percent of Italian men aged between sixteen and twenty-nine still live at home.33 And second, those who do move tend to be better educated as well. In the United States, almost half of college graduates move out of their birth states by the time they are thirty, but only 17 percent of high school dropouts do so.

Some workers, rather than dropping out of the labor market because they lack the right skills, dislike the available jobs, or live in the wrong place, will instead pursue whatever work does remain for them to do. And when this happens—where workers find themselves stranded in a particular corner of the labor market but still want a job—the outcome will not be technological unemployment, with people unable to find work at all, but a sort of technological overcrowding, with people packing into a residual pool of whatever work remains within their reach. Rather than directly cause a rise in joblessness, this could have three harmful effects on the nature of the work. The first is that, as people crowd in, there will be downward pressure on wages. Curiously, whereas technological unemployment is a controversial idea in economics, such downward pressure is widely accepted.36 At times it can be puzzling that economists tend to make such a hard distinction between no work and lower-paid work. The two are treated as unrelated phenomena—the former regarded as impossible, the latter as entirely plausible. In practice, the relationship between the two is far less straightforward. It seems reasonable to think that as more people jostle for whatever work remains for them to do, wages will fall. It also seems reasonable to think that these wages might fall so low in whatever corner of the labor market a worker is confined to that it will no longer be worth their while to take up that work at all. If that happens, the two phenomena become one. This is not an unlikely possibility: in 2016, 7.6 million Americans—about 5 percent of the US workforce—who spent at least twenty-seven weeks of the year in the labor force still remained below the poverty line.

It is sometimes said, in a positive spirit, that new technologies make it easier for people to work flexibly, to start up businesses, become self-employed, and to have a more varied career than their parents or grandparents. That may be true. But for many, this “flexibility” feels more like instability. A third of the people who are on temporary contracts in the UK, for instance, would prefer a permanent arrangement; almost half on zero-hour contracts want more regular work and job security.

Parts of our economic life already feel two-tiered in the way that Meade imagined: many of those fast-growing jobs in Figure 6.2, for instance, from retail sales to restaurant serving, involve the provision of low-paid services to the wealthy. But these “hangers-on” need not be all be “immiserated,” as Meade expected. In rich corners of cities like London and New York it is possible to find odd economic ecosystems full of strange but reasonably well-paid roles that rely almost entirely on the patronage of the most prosperous in society: bespoke spoon carvers and children’s playdate consultants, elite personal trainers and star yoga instructors, craft chocolatiers and artisanal cheesemakers. The economist Tyler Cowen put it well when he imagined that “making high earners feel better in just about every part of their lives will be a major source of job growth in the future.”41 What is emerging is not just an economic division, where some earn much more than others, but a status division as well, between those who are rich and those who serve them.

As task encroachment continues, human capabilities will become irrelevant in this fashion for more and more tasks. Take sat-nav systems. Today these make it easier for taxi drivers to navigate unfamiliar roads, making them better at the wheel. At the moment, therefore, they complement human beings. But this will only be true as long as human beings are better placed than machines to steer a vehicle from A to B. In the coming years, this will no longer the case: eventually, software is likely to drive cars more efficiently and safely than human beings can. At that point, it will no longer matter how good people are at driving: for commercial purposes, that ability will be as amusingly quaint as our productivity at hand-fashioning candles or cotton thread.

Kasparov’s experiences in chess led him to declare that “human plus machine” partnerships are the winning formula not only in chess, but across the entire economy.8 This is a view held by many others as well. But AlphaZero’s victory shows that this is wrong. Human plus machine is stronger only as long as the machine in any partnership cannot do whatever it is that the human being brings to the table. But as machines become more capable, the range of contributions made by human beings diminishes, until partnerships like these eventually just dissolve. The “human” in “human plus machine” becomes redundant.

We live in the Age of Labor, and if new tasks have to be done it is likely that human beings will be better placed to do them. But as task encroachment continues, it becomes more and more likely that a machine will be better placed instead. And as that happens, a growing demand for goods may mean not more demand for the work of human beings, but merely more demand for machines.

It is true that people in the future are likely to have different wants and needs than we do, perhaps even to demand things that are unimaginable to us today. (In the words of Steve Jobs, “consumers don’t know what they want until we’ve shown them.”)15 Yet it is not necessarily true that this will lead to a greater demand for the work of human beings. Again, this will only be the case if human beings are better placed than machines to perform the tasks that have to be done to produce those goods. As task encroachment continues, though, it becomes more and more likely that changes in demand for goods will not turn out to be a boost in demand for the work of human beings, but of machines.

We imagine that when human beings become more productive at a task, they will be better placed than a machine to perform it; that when the economic pie gets bigger, human beings will be better placed to perform the freshly in-demand tasks; that when the economic pie changes, human beings will be better placed to carry out whatever new tasks have to be done.

a fallacy that people are often accused of committing when they seem to forget about the helpful side of technological progress, the complementing force.28 The idea is an old one, first identified back in 1892 by David Schloss, a British economist.29 Schloss was taken aback when he came across a worker who had begun to use a machine to make washers, the small metal discs used when tightening screws, and who appeared to feel guilty about being more productive. When asked why he felt that way, the worker replied: “I know I am doing wrong. I am taking away the work of another man.” Schloss came to see this as a typical attitude among workmen of the time. It was, he wrote, a belief “firmly entertained by a large section of our working-classes, that for a man … to do his level best—is inconsistent … with loyalty to the cause of labour.” He called this the “theory of the Lump of Labour”: it held “that there is a certain fixed amount of work to be done, and that it is best, in the interests of the workmen, that each man shall take care not to do too much work, in order that thus the Lump of Labour may be spread out thin over the whole body of workpeople.”30 Schloss called this way of thinking “a noteworthy fallacy.” The error with it, he pointed out, is that the “lump of work” is in fact not fixed. As the worker became more productive, and the price of the washers made by him fell, demand for them would increase. The lump of work to be divided up would get bigger, and there would actually be more for his colleagues to do. Today, this fallacy is cited in discussions about all types of work. In its most general terms, it is used to argue that there is no fixed lump of work in the economy to be divided up between people and machines; instead, technological progress raises the demand for work performed by everyone in the economy. In other words, it is a version of the point that economists make about the two fundamental forces of technological progress: machines may substitute for workers, leaving less of the original “lump of work” for human beings, but they complement workers as well, increasing the size of the “lump of work” in the economy overall.

It may be right that technological progress increases the overall demand for work. But it is wrong to think that human beings will necessarily be better placed to perform the tasks that are involved in meeting that demand. The lump of labor fallacy involves mistakenly assuming that the lump of work is fixed. But the LOLFF involves mistakenly assuming that that growth in the lump of work has to involve tasks that human beings—not machines—are best placed to perform.

On average, one more robot per thousand workers meant about 5.6 fewer jobs in the entire economy, and wages that were about 0.5 percent lower across the whole economy as well. And all this was happening in 2007, more than a decade ago, before most of the technological advances described in the preceding pages.

hunter-gatherers did not pursue solitary lives of the kind that Rousseau imagined. Instead, they lived together in tribes that sometimes numbered a few hundred people, sharing the literal fruits (and meats) of their labor within their band of fellow foragers—some of whom, inevitably, were more successful in their foraging efforts than others.4 There is no forest that lets human beings retreat into perfect solitude and self-sufficiency, nor has there ever been. All human societies, small and large, simple and complex, poor and affluent, have had to figure out how best to share their unevenly allocated prosperity with one another.

technological progress does have a role in making the economic pie bigger, but the growing power of these supermanagers also allows them to take a much bigger slice of it. Forty years ago, the CEOs of America’s largest firms earned about 28 times more than an average worker; by 2000, that ratio stood at an astounding 376 times.

labor income accounting for about two-thirds of the pie and income from traditional capital making up the remaining third.27 Keynes called this “one of the most surprising, yet best-established, facts in the whole range of economic statistics” and “a bit of a miracle.” Nicholas Kaldor, one of the giants of early work on economic growth, included this phenomenon among his six “stylized facts.” Just as mathematicians build up their arguments up from indubitable axioms, he believed, so economists should build their stories around these six unchanging facts—and they did. The most popular equation in economics dealing with how inputs combine to produce outputs, the Cobb-Douglas production function, is built around the fact that the capital-labor ratio was thought to be fixed.

In the two decades since 1995, across twenty-four countries, productivity rose on average by 30 percent, but pay by only 16 percent.31 Instead of going to workers, the extra income has increasingly gone to owners of traditional capital. This “decoupling” of productivity and pay, as it sometimes known, is particularly clear in the United States, as seen in Figure 8.6. Until the early 1970s, productivity and pay in the United States were almost perfect twins, growing at a similar rate. But as time went on, the former continued upward while the latter stalled, causing them to diverge.

The OECD is quoted as saying that technology was directly responsible for up to 80 percent of the decline from 1990 to 2007, encouraging firms to shift toward using more traditional capital relative to labor.33 The IMF puts it at a more modest 50 percent in developed economies over a slightly longer period, a finding that fits with the work of other economists.34 But once you look at the explanations offered by the IMF for the rest of the decline, technological progress often has a role to play there as well. Part of this decline in the labor share, for instance, is thought to be explained by globalization, the increasingly free movement of goods, services, and capital around the world. The IMF believes that this explains another 25 percent.35 But what is actually responsible for this globalization? Technological progress, in large part. After all, it is falling transportation and communication costs that have made globalization possible.

beneath the headline story of growing inequality around the world lie three distinct trends. First, human capital is less and less evenly distributed, with people’s different skills getting rewarded to very different degrees; the part of the economic pie that goes to workers as a wage is being served out in an increasingly imbalanced way. Second, human capital is becoming less and less valuable relative to traditional capital; that part of the pie that goes to workers as a wage is also shrinking relative to the part that goes to owners of traditional capital. And third, traditional capital itself is distributed in an extraordinarily uneven fashion, an inequality that has been growing more and more pronounced in recent decades.

Inequality, then, is not inevitable. And the same is true for the economic imbalances that technological unemployment would bring about. We have the power to shape and constrain these economic divisions—if we want to.

a college degree in the United States has an average annual return of more than 15 percent, leaving stocks (about 7 percent) and bonds, gold, and real estate (less than 3 percent) trailing far behind.3 Education also does more than just help individuals: it is responsible for thrusting entire economies forward as well.

All we really know with any confidence is that machines will be able to do more in the future than they can today. Unfortunately, this is not particularly useful for deciding what people should be learning to do. But that uncertainty is unavoidable. And so we are left with just our simple rule for the moment: do not prepare people for tasks that we know machines can already do better, or activities that we can reasonably predict will be done better by machines very soon.

People will have to grow comfortable with moving in and out of education, repeatedly, throughout their lives. In part, we will have to constantly reeducate ourselves because technological progress will force us to take on new roles, and we will need to train for them. But we will also need to do it because it is nearly impossible right now to predict exactly what those roles will be. In that sense, embracing lifelong learning is a way of insuring ourselves against the unknowable demands that the working world of the future might make on us.

The question of whether universities are “just selecting for talented people who would have done well anyway … isn’t analyzed very carefully,” Thiel complains.24 In fact, though, many economists have spent large portions of their lives thinking specifically about this issue. The problem is so popular that it has its own name: “ability bias,” a particular case of what’s known in econometrics as “omitted variable bias.” (In this case, the omitted variable is a person’s innate ability: if higher-ability people are more likely than others to go to university in the first place, then attributing their greater financial success to their education alone leaves out a significant part of the story.) Economists have developed a tool kit of techniques to address this omission, and their sense—contrary to Thiel’s—is that even once ability bias is accounted for, universities still appear to have a positive impact. Talented people might earn more than others in any case, but education helps them earn even more than they would otherwise.

It would be nice to think that as human beings we are all infinitely malleable, entirely capable of learning whatever it is that is required of us. And you might argue that the difficulty of education is no reason to avoid it. After all, did President Kennedy not say that we do important things “not because they are easy, but because they are hard”?28 The thrust of Kennedy’s comment may be right. But we have to temper our idealism with realism. If “hard” turns out to mean impossible, then inspirational rallying cries to reeducate and retrain are not helpful.

In calling for a Big State, however, I mean something different: not using the state to make the pie bigger, as the planners tried and failed to do, but rather to make sure that everyone gets a slice. Put another way, the role for the Big State is not in production but in distribution.

A more practical difficulty is that the idea of taxing traditional capital is very ambiguous, far more so than taxing labor. Recently, public discussion has veered toward so-called robot taxes. Bill Gates is partly responsible for this, having caused a stir with his views on the subject. “Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed,” he said in a recent interview. “If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level.”

And perhaps most important, we must remember that technological progress (of which robots are a part) drives economic growth—it makes the economic pie bigger in the first place. That is why Larry Summers calls the robot tax “protectionism against progress.”17 A robot tax might mean fewer robots and more workers, but it might also mean a smaller pie as well.

The wide range of support for the UBI disguises the fact that key details of it are subject to uncertainty and disagreement. For instance, how are payments made? UBI supporters often argue that payment in cash is a “fundamental” part of their proposal, but in practice there are other reasonable ways to make people more prosperous.34 One approach, for instance, is to make important things available in society at no cost: rather than just give people cash, the state in effect makes certain purchases on their behalf. Already in the United States, about forty million people use the Supplemental Nutrition Assistance Program, or “food stamps,” to receive basic sustenance for free, worth about $1,500 a year.35 In England, health care and primary and secondary education are free for everyone who wants them, each worth thousands of pounds per year.36 Add up such initiatives, and you end up with a sort of UBI—though one that the state has already spent for you. And if the income payments do get made in cash, how generous should they be? The UBI says “basic.” But what does that mean? Some economists think it implies a minimal payment, not very much at all. John Kenneth Galbraith, for instance, said that introducing “a minimum income essential for decency and comfort” is the right thing to do.37 Friedrich Hayek similarly spoke of “a certain minimum income for everyone.”38 Today’s prominent UBI advocates often agree. Annie Lowrey, author of Give People Money, makes the case for “just enough to live on and not more”; Chris Hughes, author of Fair Shot, argues for $500 a month.39 But there are others who feel differently. Philippe Van Parijs, today’s leading UBI scholar, wants to use UBI to build a “truly free” society, where people are not tied down by what they earn. That is a far loftier goal than what is envisaged by Galbraith and Hayek—and a far more expensive one, too.

As we approach a world with less work, this sort of struggle over who counts as a member of the community will intensify. The Native American experience shows that dealing with questions of citizenship is likely to be fractious. The instinct in some tribes was to pull up the drawbridge—a reaction we can see in other settings, too. Consider the financial crisis in 2007 and its aftermath. As economic life got harder, the rhetoric toward immigrants in many countries hardened as well: they were said to be “taking our jobs,” “running down our public services.” There was a collective impulse to narrow the boundaries of the community, to restrict membership, to tighten the meaning of ours. In much the same way, support for so-called welfare chauvinism—a more generous welfare state, made available to fewer people—is on the rise. In Europe, for example, a survey found “both rising support for redistribution for ‘natives’ and sharp opposition to migration and automatic access to benefits for new arrivals.”

UBI advocates argue that universal payments remove any stigma associated with claiming support. If everyone receives the payments, nobody can be labeled by society as a “scrounger” and no individual will feel ashamed to have to claim theirs. As Van Parijs puts it, “There is nothing humiliating about benefits given to all as a matter of citizenship.”

The UBI fails to take account of these responses. It solves the distribution problem, providing a way to share out material prosperity more evenly; but it ignores this contribution problem, the need to make sure that everyone feels their fellow citizens are in some way giving back to society. As the political theorist Jon Elster put it, the UBI “goes against a widely accepted notion of justice: it is unfair for able-bodied people to live off the labor of others. Most workers would, correctly in my opinion, see the proposal as a recipe for exploitation of the industrious by the lazy.”

There are two reasons why sharing out capital might be attractive. The first is that it would reduce the need for the Big State to act as an income-sharing state. If more people owned valuable capital, income would flow more evenly across society of its own accord. The second reason is that such sharing would also help to narrow economic divisions in society. If the underlying distribution of capital stays the same, and the state only shares out income, then profound economic imbalances will remain. If left unresolved, such divisions could turn into noneconomic strife: ruptures of class and power, differences in status and respect.56 By sharing out valuable capital, and directly attacking the economic imbalances, the state could try to stop this from happening.

the state’s labor-supporting efforts should be focused primarily on changing the actual incentives that employers face, forcing closer alignment between their interests and those of the society of which they are a part.

For Schumpeter, economics was all about innovation. He called it the “outstanding fact in the economic history of capitalist society.” His argument for monopolies is that, were it not for the prospect of handsome profits in the future, no entrepreneur would bother to innovate in the first place. Developing a successful new product comes at a serious cost, in both effort and expense, and the possibility of securing monopoly power is the main motivator for trying at all. It acts as the “baits that lure capital on to untried trails.”22 Moreover, monopoly profits are not simply a consequence of innovation, but a means of funding further innovation. Substantial research and development very often draws on the deep pockets established by a company’s past commercial successes.

In the twentieth century, our main preoccupation was with the economic power of large companies. But in the twenty-first, we will increasingly have to worry about this political power as well.

From this viewpoint, the threat of technological unemployment has another face to it. It will deprive people not only of income, but also of significance; it will hollow out not just the labor market, but also the sense of purpose in many people’s lives.1 In a world with less work, we will face a problem that has little to do with economics at all: how to find meaning in life when a major source of it disappears.

Take Alfred Marshall, another giant of economic history. He proclaimed that “man rapidly degenerates unless he has some hard work to do, some difficulties to overcome,” and that “some strenuous exertion is necessary for physical and moral health.” To him, work was not simply about an income, but the way to achieve “the fullness of life.”3

Jahoda and her colleagues wanted to know what the impact of such widespread worklessness would be. Their methods were unconventional: to collect data on residents without making them realize they were being watched, the researchers embedded themselves in everyday village life. (Their various enterprises included a clothes cleaning and repair service, parent support classes, a free medical clinic, and courses in pattern design and gymnastics.) What they found was striking: growing apathy, a loss of direction in life, and increasing ill will to others. People borrowed fewer library books: 3.23 books on average per resident in 1929, but only 1.6 in 1931. They dropped out of political parties and stopped turning up to cultural events: in only a few years, the athletic club saw membership fall by 52 percent and the glee club by 62 percent. Unemployment benefits required that claimants do no informal work; in those years, Marienthal saw a threefold increase in anonymous denunciations of others for breaking that rule, yet almost no change at all in the total number of complaints that were judged well-founded. Researchers watching at a street corner even noted a physical change: men without work walked more slowly in the street and stopped more frequently.

Work matters not just for a worker’s own sense of meaning; it has an important social dimension as well, allowing people to show others that they live a purposeful life, and offering them a chance to gain status and social esteem.

For those with a job, the connection between work and meaning is wonderful: in return for their efforts, they get both an income and a sense of purpose. But for the unemployed, this link may become instead a source of further discomfort and distress. If work offers a path toward a meaningful life, the jobless may feel that their existence is meaningless; if work provides status and social esteem, they may feel out of place and deflated. This may partly explain why the unemployed often feel depressed and shamed, and why their suicide rate is about two and a half times the rate of those in work.10 A prevailing political philosophy of our time, the idea of meritocracy, does little to help.11 This is the notion that work goes to those who somehow deserve it, due to their talents or effort. Yet if work signifies merit, then those without it might feel meritless. Michael Sandel once quipped that in feudal times, at least those at the top knew that their economic fortunes were a fluke of birth, the simple brute luck of being born into the right family—whereas today, the most fortunate imagine they actually merit their positions, that being born with the right talents and abilities (and, often, supportive and prosperous parents) has nothing to do with luck at all.12 An unpleasant corollary is that the less fortunate now often think they merit their bad luck as well.

Aristotle, likewise, wrote that “citizens must not lead the life of artisans or tradesmen, for such a life is ignoble and inimical to excellence.”22 He believed that meaning could only come through leisure, and that the only purpose of work is to pay for leisure time: “We work in order to enjoy leisure, just as we make war in order to enjoy peace.”23 In fact, the Greek word for “work,” ascholia, literally means “the absence of leisure,” schole; for the Greeks, leisure came first, the opposite from how many think today.24

Work is a source of meaning for some people at the moment not because work itself is special, but because our jobs are where we spend the majority of our lives. We can only find meaning in what we actually do—and freed up to spend our lives differently, we will find meaning elsewhere instead.

Today, that sense of value is overwhelmingly shaped by the market mechanism: a thing’s value is the price that someone is willing to pay for it, and a worker’s worth is the wage that they receive. For all its flaws, there is something extraordinary about the inexorable simplifying power of this mechanism. In the white heat of the market, the clash between people’s infinite desires and the hard reality of satisfying them gets boiled down to a single number: a price.

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Key Points from The Costs of Inequality in Latin America

The Costs of Inequality in Latin America: Lessons and Warnings for the Rest of the World by Diego Sánchez-Ancochea

Inequality has historically contributed to many social ills, from low economic growth to weak democratic institutions and high levels of violence. Populism, financial crises, bad jobs, social polarization: Latin America has struggled with all these problems for more than a century.

It is not only that inequality has shaped political and economic institutions in Latin America; these institutions have in turn contributed to more inequality. For example, labor market duality (with large differences between good and bad jobs) has led to growing income gaps between workers. Politics and economics have also reinforced each other: inequality has contributed to the election of leaders who, in their search for easy solutions, have ended up triggering economic crises and ultimately favoring the wealthy.

All Latin American countries have a similar history: they became colonies of Spain and Portugal during the sixteenth century, gained independence during the nineteenth century, and then had to deal with similar obstacles to building effective institutions. The state is weaker than in developed countries: corruption is an endemic problem, changes in rules and regulations are common, and policies are often inconsistent. They have always had to deal with influential external actors, including the US, and struggled with external dependence. Latin American countries also share some cultural traits, including a common language (with the exception of Brazil … but Portuguese and Spanish are similar), and many social similarities. They are some of the most urbanized countries in the developing world, and most have large non-white minorities.

the lack of economic dynamism has much to do with the control of policymaking by the wealthy—politics and economics can seldom be separated. The top 1 percent successfully pressured for low taxes: most Latin American states collect less than they should, given their level of development.

given these exclusionary policies and lack of economic dynamism, it is not surprising that citizens have repeatedly supported populist responses. Leaders like Juan Domingo Perón in Argentina in the 1940s and 1950s or Hugo Chávez in Venezuela more recently promised to provide good jobs and adequate social benefits to the poor and the urban middle classes. Unfortunately, their governments often ended up implementing unsustainable economic policies, while proving unable or unwilling to systematically confront the power of the wealthy—a lesson voters in developed countries would do well to remember.

Inequality, labor market dualism, financial crises, and political instability are growing everywhere. Wealthy countries like the US look more and more like Latin America and—if they do not reverse direction—could suffer many of the same negative interactions over the long run. Unfortunately, avoiding the costs of inequality will become harder and harder: Latin America shows that reversing the income gap is difficult precisely because of negative political and economic feedbacks.

Between 2003 and 2013, most Latin American countries improved their distribution of income, precisely at a time when income gaps were increasing in the rest of the world. Some of the drivers of this recent improvement are not particularly relevant for wealthy economies but may be important for developing countries. In some Latin American countries, the poor became the subject of social rights for the first time in history, contributing to a rapid increase in their income. The power of electoral competition to promote some level of inclusion is a second useful lesson.

Maybe the distribution of income was no worse in Latin America in the middle of the nineteenth century than in England during the Industrial Revolution; rapid economic transformation in Manchester, London, and other British cities did create wealthy winners and millions of losers, as Karl Marx eloquently explained. Yet it is clear that in Latin America institutions and policies were organized in favor of the powerful from early on: huge latifundios, underinvestment in primary education, and restrictive voting rights were the norm across the region.

When the first wave of globalization met Latin America’s traditional institutions, inequality accelerated. In Argentina, Brazil, Chile, and Uruguay the Gini coefficient increased by at least five percentage points between 1870 and 1920. Rich landowners found new opportunities to make large amounts of money through exports.

governments without resources cannot fund much-needed investments in social programs and infrastructure.

Latin America’s problems, including uneven investment in education, difficulties in taxing the wealthy, economic crises, and informal labor markets, are increasingly evident in other parts of the world and seem more widespread and entrenched than ever.

the elite had no incentive to innovate. Exporting agricultural goods and/or various mining products was much more profitable than trying to produce manufactures. Between 1850 and 1912, primary exports grew by an annual average of 3.3 percent with a particularly fast expansion in Argentina (6.1 percent) and Uruguay (5.6 percent). Meanwhile, the manufacturing sector struggled to become more productive.

The contrast with East Asia could not be more striking. In Latin America inequality contributed to underinvestment in education which, in turn, hampered the creation of a more dynamic economy. Countries such as South Korea, Taiwan, and Singapore—historically less unequal—have invested many resources in primary and secondary education since the middle of the twentieth century. A more skilled labor force helped them to promote the new economic sectors (from heavy industry to semiconductors) that were behind their economic miracle.

“for the most part, the children of the politically influential people attend private primary and secondary schools. Thus they do not directly feel the deficiencies of the public school system, because their interests are not directly and immediately affected by the success or failure of public schools. This reduces the sense of urgency that might otherwise lead influential parents to press decision makers to make tough policy choices.”

Latin America the private sector is responsible for only one-third of total spending in research and development (R&D) compared to 50 percent in Asia and 70 percent in the OECD.

the large economic gap between a few powerful business groups with limited interest in innovation and a large number of small, unproductive firms with no resources to invest is behind Latin America’s backward position. In 2011, spending in research and development was just 0.33 percent of GDP; in contrast, it was 1.1 percent in Asia and 2.0 percent in wealthy countries. The number of licenses and patents—which have increased rapidly in Asia in the last decade—is also extremely low. Unfortunately, without innovation it is hard to sustain economic growth and a dynamic transformation of the economy.

First, the elite’s control of mass media—both newspapers and TV—has allowed them to shape public debate and build opposition against tax hikes. Multiple op-eds and TV programs repeat the same half truths about the negative impact of taxes on investment, the link between taxes and corruption, and the need to limit state regulation.

Second, the use of revolving doors is quite common in Latin America. Entrepreneurs and business managers spend their professional lives moving from the private to the public sector and back.

Third, as we saw in the case of Chile, the business elite often has close links to political parties, contributing to their campaigns and lobbying through formal and informal channels. Although most governments have advanced in the regulation of campaign financing, large corporations and powerful individuals still transfer millions of dollars to presidential and legislative candidates.

Social programs not only compensate for the negative effects of external shocks and economic adjustment, but also enhance competitiveness. Universal social policies promote human capital, expand aggregate demand, and improve social capital. They contribute to higher economic growth and the creation of more dynamic sectors—something Costa Rica has shown for years. On the other hand, underfunded states are more prone to economic crises and may be regularly forced to implement costly austerity policies.

In unequal societies like those in Latin America, reaching agreement on economic adjustment between different social groups—which are far apart in terms of income and worldview—is almost impossible; there is too much distrust and not enough social cohesion. Confronted with such difficulties, governments prefer to maintain the status quo, hoping that the economic situation improves or postponing painful reforms until another administration is elected. Rodrik tests these arguments with a series of quantitative exercises, finding a positive correlation between income inequality and bad macroeconomic policies as well as between income inequality and economic growth collapses.

Keynes’s recipe—which has worked more often than not—has seldom been followed in Latin America. Instead, in times of crisis most governments have reduced public investment, cut social programs, and increased interest rates—making the construction of factories more expensive. In doing so, they have contributed to unemployment, poverty increases, and a further worsening of the income gap. Pressures from international institutions like the IMF and the World Bank, lack of access to borrowing, and right-wing ideology explain these mistaken policy choices.

In the US, the Republican Party has allied with the business elite to promote regressive tax reforms. Democrats have failed to systematically campaign against tax reductions, partly because they are increasingly dependent on funds from the wealthy. Even in more equal countries, growing income concentration has gone hand in hand with lower public revenues:

A direct relationship between a charismatic leader and the people, a certain disregard for stable institutions and political parties, and the promotion of anti-elite grievances characterized Peronism. A similar type of leadership—which many have called populist—is evident in Latin America across time and space: from Getúlio Vargas in Brazil in the 1940s to Hugo Chávez in Venezuela in the 2000s.

Inequality has thus contributed to weak, unresponsive and/or unstable political institutions which, in turn, lead to a further worsening of income distribution through several channels. First, populist experiments have often resulted in economic crises with significant costs for the low-income majority. Second, right-wing authoritarian governments have consistently adopted regressive measures, thus benefiting the wealthy significantly. In Pinochet’s Chile, for example, the Gini coefficient increased from less than 44 in the early 1970s to 59 in 1988. Third, weak democracies have protected the wealthy and often failed to significantly expand economic and social rights.

The economic elite feared people’s participation in politics; they were worried that poor voters would elect leaders who supported high income taxes and generous social programs. As a result, they did everything in their power to avoid real democracy.

As Perón’s authoritarian tendencies intensified, the Argentinian democracy entered into the kind of death spiral that political scientists Steven Levitsky and Daniel Ziblatt describe in their book How Democracies Die.25 A constitutional reform allowed the re-election of the president, strengthened presidential power, and modified electoral rules in Perón’s favor.

If populism contributed to inclusion and participation, why do I regard it as a political cost of inequality? The answer is simple: populist movements often triggered political instability and institutional volatility. Perón, Vargas, and the other populists loved conflict: politics was all about black and white, the “people” against the “oligarchs,” and the “nation” against the “empire.” While this was partly understandable in the context of income concentration, it left little room for debate and compromise.

Why did the business elite consent to, and at times even support, democratization? Two reasons are particularly relevant. First, authoritarian regimes became less attractive.

Second, the business elite made sure that the new regimes would not harm their economic interest and political power.

the expansion of neoliberal policies became a safeguard for the elite.54 On the one hand, economic liberalization reduced the freedom of new governments to adopt radical policies. If a leftist president increased taxation or the minimum wage significantly, it had to deal with capital flight and other economic difficulties. On the other hand, the World Bank and other powerful international lenders forced countries to accept neoliberal policy packages. Some areas, such as monetary policy, also moved outside governments’ purview, as central banks across the region became independent.

Protests against the expansion of markets and the lack of true democracy gradually increased.

Soon enough, street protests translated into the election of left-of-center presidents in what was then called “the Pink Tide.”

The Latin American experience illustrates the high political costs of inequality in the political sphere. The wealthy have always refused to support real democracy for fear of redistribution. In response, the poor have often searched for populist solutions that have failed to change power relations or strengthen democratic institutions over the long run. The problems, however, do not finish here: limited democracies, populism, and authoritarianism have in turn contributed to more inequality.

Populist regimes’ contribution to income distribution over the long run has also been problematic for at least three reasons. First, in their attempt to improve things, some populist governments have ended up triggering costly financial crises. Perón’s first administration provides a good illustration of this problem. In his first years in office, real wages increased by 50 percent. Initially, this expansion did not create problems because international agricultural prices—and, as a result, Argentinian exports—were growing quickly as well. Yet when external conditions deteriorated in 1949, Argentina moved from a trade surplus to a deficit and the country’s reserves were quickly depleted. Resources for industrialization also dried up as agriculture entered into crisis. Perón initially increased the money supply to keep the economy going, resulting in a 31 percent inflation rate in 1949. In the end, the attempt to redistribute income too quickly led to a crisis that forced a painful—and inequality-inducing—stabilization of the economy after 1950.76 The Brazilian progressive economist and former Minister of Finance Luiz Carlos Bresser-Pereira offers a compelling explanation of the “populist cycle.” Initially, governments adopt expansionary policies, including a strong exchange rate (to make imports cheap), higher public expenditure, and higher wages. Not surprisingly, economic growth accelerates as consumption and investment increase. Unfortunately, the positive trend is short-lived. Little by little—or quite quickly, if there is also a negative external shock like the one Argentina suffered under Perón—all economic indicators deteriorate: a trade deficit appears as imports grow faster than exports. The budget deficit increases as spending surpasses taxes. The money supply and inflation follow. All these problems consistently lead “to a severe crisis, sometimes accompanied by, at least, a change of ministers if not a coup d’état and inevitably … a radical change in economic policy.” The impact of the crisis and of the neoliberal policies that follow on income distribution is often catastrophic.

Second, many populist leaders have shown little interest in building independent social movements. From Brazil under Vargas to Venezuela under Chávez, most governments of this kind have coopted trade unions and limit dissent.

Third, the incessant polarization promoted by populists is hard to sustain over the long run. All these leaders have mastered the art of dividing the country between the “people” and the oligarchy, between a majority of supporters and a minority of enemies. Think about Hugo Chávez proclaiming “It is not me, it is the people” or Rafael Correa joyfully shouting “Ecuador voted for itself” after his 2009 electoral victory.79 Although their strategy may at times have been justified (there is a sharp gulf between the winning elite and the rest in Latin America), it has consistently generated mistrust and conflict.

The French economist Thomas Piketty suggestively links this political convergence on economic ideas to transformations in the party system: the US and many European countries have moved, he argues, from class-based competition to competition between different elites. In the 1950s and 1960s, left-wing parties were supported by unskilled, poorly paid workers, while most high-income, high-educated individuals voted for the right. Since the 2000s, left-wing parties have become an instrument of the “intellectual elite,” while right-wing parties are primarily supported by high-income individuals (the “business elite”). Meanwhile, low-income voters no longer feel represented by mainstream political parties, searching for more radical alternatives instead.

In violent societies, low-income groups suffer disproportionately, both emotionally and financially. In segregated societies, there are large education gaps between rich and poor and few cross-class social networks. In mistrustful societies, the opportunities to build coalitions between the poor and the middle class—required to expand redistributive social programs—are severely hampered. In this way, structural inequality has perpetuated itself in Latin America through various social vicious circles.

Why do Latin Americans mistrust institutions so much? There are many reasons, including high levels of corruption and poor public service delivery. Yet inequalities in income and political opportunities are probably primary drivers. Most people resent their place in society and do not feel represented by political institutions; in fact, almost four out of five Latin Americans are convinced that governments rule for the benefit of the powerful and are uninterested in the preferences of voters—a much higher percentage than in other parts of the world.

low-trusting individuals were less likely to support equity-enhancing state interventions. These groups are likely to believe that children from other families will not put in the effort to learn and achieve good results. They often see the poor as cheaters and, as a result, do not support redistributive programs.77 Unfortunately, this triggers another vicious circle: inequality leads to low trust which, in turn, prevents citizens from supporting redistributive policies, thus resulting in even more inequality.

As the sociologist Robert Putman famously noted in the early 2000s—after two decades of growing inequality—Americans are now “bowling alone,” living solitary lives and finding few opportunities to mix with other classes.85 In this context, political polarization has intensified, making the creation of cross-class coalitions in support of redistribution harder than ever.

Structuralism provided a new interpretation of inequality. In their view, the only way to understand income distribution within each Latin American country was to consider the way the global economy was organized. Most technological innovations took place in countries like the UK, France, and the US—the “center”—and in many sectors simultaneously. These economies not only grew more but were also diversified and had many high-productivity sectors. Workers in all kinds of activities, from textiles to steam engines and from wine to steel-making, were highly productive and, as a result, relatively well paid. In contrast, countries in Latin America, Africa, and much of Asia—the “periphery”—were specialized in agricultural and mining products and relied on innovations from abroad. In the periphery, many economic activities—particularly within the manufacturing sector—were underdeveloped and most wealth was concentrated in the export sector. Most jobs were informal and poorly paid.

Formal and informal education, Freire argued, reproduces oppression and limits dissent. He warned against the “banking concept of education,” which is based on the idea that the primary goal of students is to accumulate knowledge. “Instead of communicating, the teacher issues communiques and makes deposits which the students patiently receive, memorize, and repeat,” Freire explained in Pedagogy of the Oppressed.14 According to this model, students should focus on memorizing the multiplication tables, the capitals of the world, and all kinds of historical facts—but should not be encouraged to question why this is useful or important.

A liberating education should be built on dialogue. Students should participate in the process at every step of the way: when designing the curriculum, when learning new concepts, and when doing problem-solving exercises. The process of dialogue should help to break the barriers between teacher and pupil, promoting a less authoritarian and more democratic relationship. The good teacher should always link theory and practice, allow students to question her/him and promote joint discovery. How different is this approach from the way most schools are organized even today! The end goal of education in contexts of oppression and inequality is to free students from dominant thinking, encouraging them to change the world. Freire often called for a transformative education that simultaneously encouraged respect. “The ideal is to promote the transformation of rebellious consciousness into revolutionary consciousness. To be radical without becoming sectarian. To be strategic without becoming cynical. To be skillful without becoming opportunistic. To be ethical without becoming puritanical,” he eloquently explained to his niece.

what are the factors that influence your income? Economists usually consider three. Factor 1: the wealth and education you have—often called endowments. Some people have a lot of property, stocks, and other financial assets. Others have no savings at all. Large landowners coexist with individuals with no land. Some people enter the labor market after years of study, while other workers have not even finished primary education. Factor 2: the income you make from your job and all your assets. Introductory economics books use the concepts of supply and demand in different markets to explain this. For example, your wage depends on how many people with a similar level of education to yours there are. If your skills are in high demand and there are few people like you, your wage will be relatively high. By contrast, if you do not have unique skills, you will face competition from many others. If you own land in a highly sought-after area of a city, you can rent it at a higher price than if it is in an unpopular one.

Factor 3: the redistribution of income through taxes and social spending. Your final income will depend not only on your wages and the return of your savings, but also on how many taxes you pay and which social benefits you receive. In most but by no means all countries, taxes and social spending together contribute to reducing the income gap between rich and poor.

“There’s just no evidence that raising the minimum wage costs jobs,” explains the Nobel Prize-winning economist, Paul Krugman—instead they lead to “better morale, lower turnover, increased productivity.”

History tells us that an active state that interacts constructively with the private sector is particularly important. Most examples of successful economic transformation—Scandinavia, South Korea, Taiwan, China—relied on what economists call industrial policy. Even in the US, often (wrongly!) considered a free market paradise, the state drove most significant innovations—as convincingly shown by Mariana Mazzucato in The Entrepreneurial State.

The impact of this process of financialization on inequality across the world cannot be overstated. “The success or failure of the financial sector has had serious effects on the rest of the economy and most of its returns have gone to the wealthy driving inequality,” argues a contributor to the market-friendly Forbes magazine.32 Four channels have been particularly important. First, by pressuring companies to produce short-term profits, financial markets have contributed to periodic firings, growing flexibility in labor arrangements, and low wage growth. Second, high wages in the financial market have been one of the drivers of inequality at the top. For example, financial deregulation contributed to a 20 percent increase in the pre-tax earnings of the wealthiest 10 percent in the UK and a 10 percent growth in Japan.

To be effective, democracy has to go beyond free, regular elections. Truly democratic institutions must provide political equality: every citizen must have access to high-quality information and to a say—at least potentially—in the political process. This means that mass media cannot be controlled by a few interest groups and that the right to mobilize, to protest, and to organize must be protected. Campaign financing has to be regulated so that powerful individuals cannot buy presidential candidates or congressional votes. Political parties must be both strong and diverse so that they can exert effective opposition when they are not in power and can also offer true policy alternatives to choose from.

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Key Points from Narrative Economics by Robert Shiller

We can think of history as a succession of rare big events in which a story goes viral, often (but not always) with the help of an attractive celebrity (even a minor celebrity or fictional stock figure) whose attachment to the narrative adds human interest.

Contagion is strongest when people feel a personal tie to an individual in or at the root of the story, whether a stock personality type or a real celebrity.

Narrative economics demonstrates how popular stories change through time to affect economic outcomes, including not only recessions and depressions, but also other important economic phenomena. The idea that house prices can only go up attaches to the stories of rich house flippers seen on television. The idea that gold is the safest investment attaches to stories of war and depression. These narratives have a contagious element, even if their attachment to any given celebrity is tenuous.

The overriding theme is that most people have little or nothing to say if you ask them to explain their objectives or philosophy of life, but they brighten at the opportunity to tell personal stories, which then reveal their values.

Economic narratives follow the same pattern as the spread of disease: a rising number of infected people who spread the narrative for a while, followed by a period of forgetting and falling interest in talking about the narrative.4 In both medical and narrative epidemics, we see the same basic principle at work: the contagion rate must exceed the recovery rate for an epidemic to get started.

Just as the world experiences co-epidemics of diseases, where two or more diseases interact positively with each other, we also see co-epidemics of narratives in which the narratives are perceived as sharing a common theme, such as case studies that illuminate a political argument, creating a picture in the mind that is hard to see if one focuses on just one of the narratives. In other words, large-scale economic narratives are often composed of a constellation of many smaller narratives. Each smaller narrative may suggest a part of a larger story, but we need to see the full constellation to discern the full theme.

Hence narratives that seem contrary to prevailing thought may have lower contagion rates that do not result in epidemics.

In addition to a constellation of narratives, there is a confluence of narratives that may help drive economic events. By a confluence, I mean a group of narratives that are not viewed as particularly associated with one another but that have similar economic effects at a point in time and so may explain an exceptionally large economic event. For example, in my 2000 book Irrational Exuberance, I listed a dozen precipitating factors, or narratives, that happened to occur together around 2000 to create the most elevated stock market in the United States ever, soon to be followed by a crash. The list, in brief, comprised the World Wide Web, the triumph of capitalism, business success stories, Republican dominance, baby boomers retiring, business media expansion, optimistic analysts, new retirement plans, mutual funds, decline of inflation, expanding volume of trade, and rising culture of gambling. If we want to know why an unusually large economic event happened, we need to list the seemingly unrelated narratives that all happened to be going viral at around the same time and affecting the economy in the same direction. However, it is important to recognize that big economic events usually can’t be described as caused by just a single constellation of narratives. It is far more likely that big economic events are not explainable in such satisfying terms. Instead, explaining those events requires making a list of economic narratives that itself cannot be described as a simple story or a contagious narrative.

1938 the existentialist philosopher Jean-Paul Sartre wrote, A man is always a teller of tales, he lives surrounded by his stories and the stories of others, he sees everything that happens to him through them; and he tries to live his life as if he were recounting it.1

Ultimately, the story’s rich visual imagery helped it evolve from an economic anecdote into a long-term memory. The visual detail of the napkin may have lowered the speed at which people forgot the narrative, which could have helped the epidemic penetrate a large fraction of the population. There is a lesson to be learned here for those who want their stories to go viral: when authors want their audience to remember a story, they should suggest striking visual images. In ancient Rome, the senator Cicero advocated the use of this strategy, quoting the scholar Simonides: For Simonides, or whoever else invented the art, wisely saw, that those things are the most strongly fixed in our minds, which are communicated to them, and imprinted upon them, by the senses; that of all the senses that of seeing is the most acute; and that, accordingly, those things are most easily retained in our minds which we have received from the hearing or the understanding, if they are also recommended to the imagination by means of the mental eye.

people tended to share content that enhances self-related thoughts—that is, information that “engages neural activity in regions related to such processes [self-presentation or mental concept], especially in medial prefrontal cortex,” and that “involves cognitions or forecasts about the mental states of others.”3 In other words, these people are more willing to share their health information in the form of stories about themselves and others.

The polymath David Hume (1711–76) wrote in 1742: When any causes beget a particular inclination or passion, at a certain time and among a certain people, though many individuals may escape the contagion, and be ruled by passions peculiar to themselves; yet the multitude will certainly be seized by the common affection, and be governed by it in all their actions.9

Gustave Le Bon said in his book Psychologie des foules (The Crowd, 1895), “Ideas, sentiments, emotions, and beliefs possess in crowds a contagious power as intense as that of microbes.”

In a competitive market in which competitors manipulate customers, and in which profit margins are competed away to normal levels, no one company can choose not to engage in similar manipulations. If they tried, they might be forced into bankruptcy. A phishing equilibrium with a certain acceptable level of dishonesty in narrative is therefore established.14 Phishing equilibria may not be all that bad. In the case of the book cover, there has developed an art of book jackets that sometimes have significant value.

All these examples illustrate a fundamental error that people tend to make: phools think that the popularity of a story or of a brand is evidence of its quality and deep importance, when in fact it rarely is. On the contrary, growing evidence in recent years has shown that many consumers detest logos and aggressive marketing.15 Narrative contagion is often the result of arbitrary details, such as the frequency of meetings among people (many people see a logo on a shirt) and natural links to other contagious narratives (Lacoste’s onetime fame as a tennis player).

Framing is related to the Daniel Kahneman and Amos Tversky representativeness heuristic (1973), whereby people form their expectations based on some idealized story or model, judging these expectations based on the prominence of the idealized story rather than estimated probabilities. For example, we may judge the danger of an emerging economic crisis by its similarity to a remembered story of a previous crisis, rather than by any logic.

Psychologists have also noted an affect heuristic, whereby people who are experiencing strong emotions, such as fear, tend to extend those feelings to unrelated events.26 Sometimes people note strong emotions or fears about possibilities that they know logically are not real, suggesting that the brain has multiple systems for assessing risk. This “risk as feelings” hypothesis holds that some primitive brain system more connected to palpable emotions has its own heuristic for assessing risk.

Though modern economists tend to be very attentive to causality, as a general rule they do not attach any causal significance to the invention of new narratives. I want to argue here not only that causality exists, but also that it goes both ways: new contagious narratives cause economic events, and economic events cause changed narratives.

Psychologists have studied how the brain chooses which memories to give flashbulb status, analogous to choosing which photos to put in a family album. It turns out that flashbulb memories are connected not only to the emotions attached to the remembered event but also to social psychological factors. Memories that involve a shared identity with others, or that are rehearsed with others, are more likely to achieve flashbulb status.14 Thus flashbulb memories are selected in a way that gives them a better chance to be involved in the formation of contagious narratives.

In attempting to be vivid, storytellers often resort to fiction or fake news, thereby providing amplified tales. The history of narratives shows that “fake news” is not new. In fact, people have always liked amusing stories, and they spread stories that they suspect are not true, as for example in urban legends. In fact, people often spread titillating stories without making any clear moral decision whether they are spreading falsehoods or not.

Ultimately, the mass of people whose consumption and investment decisions cause economic fluctuations are not very well informed. Most of them do not view or read the news carefully, and they rarely get the facts in any discernible order. And yet their decisions drive aggregate economic activity. It must be the case, then, that attention-getting narratives drive those decisions, often with an assist from celebrities or trusted figures.

Proposition 1: Epidemics Can Be Fast or Slow, Big or Small

The contagion rate also varies greatly from one narrative epidemic to another. One example of a narrative epidemic with very high contagion might be that of a national emergency, like the start of a war. With such narratives, people feel that the story is so important that they have license to interrupt any other conversation with the news, or to speak with people with whom they do not normally communicate. An example of a successful narrative with a very low contagion rate might be a patriotic story illustrating a country’s national greatness, a story that is brought up only at appropriate times at home, in the classroom, or at events sponsored by civic organizations. Such a narrative can develop (slowly) into a huge epidemic if the forgetting rate is low enough.

high contagion parameter and a low recovery rate mean that almost the whole population eventually hears the narrative, sometimes very quickly. But the same narrative can reach most of the population rather slowly if the contagion parameter is low but the recovery rate is even lower.

Proposition 2: Important Economic Narratives May Comprise a Very Small Percentage of Popular Talk

On their own, any individual, vague narratives might not have determined behavior, but a constellation of such narratives may have.

Proposition 3: Narrative Constellations Have More Impact Than Any One Narrative

Proposition 4: The Economic Impact of Narratives May Change Through Time

We must pay attention to the names that people attach to their narratives. Seemingly minor changes in the name of a narrative can matter a lot, especially if the new name attaches to a different constellation of narratives. In linguistics, synonyms never have exactly the same meaning. If pressed, people can state complex thoughts about the slightly different connotations of synonyms. In neurolinguistics, synonyms have different connections in the neural network. Some of those connections can matter a lot in terms of the economic ideas they support.

Proposition 5: Truth Is Not Enough to Stop False Narratives

according to political scientist Stephen Van Evera (1984), World War I started at least partly because a false narrative, which he calls “the Cult of the Offensive,” went viral. This narrative was a theory that the country that moves first to attack another country will generally have the advantage. The idea was supported by some historical narratives and illustrated by simplistic psychological, mathematical, and bandwagon arguments. Ultimately, Van Evera argues, this theory led to instability: everyone wanted to attack first. Germany thought it had a “window of opportunity” to successfully pursue a “preventive war” against Russia. But the narrative was wrong. It had economic consequences—a huge arms race—and resulted in a war that was disastrous for both the offense and the defense. Norman Angell called the narrative “The Great Illusion” in a 1911 book with that title. Angell’s ideas were convincing to many (and he later won the Nobel Peace Prize for his work), but they did not go viral fast enough to prevent the war. The illusion won out even after it had been decisively disproven, because the proof did not spread as fast as the illusion did.

Ultimately, a story’s contagion rate is unaffected by its underlying truth. A contagious story is one that quickly grabs the attention of and makes an impression on another person, whether that story is true or not.

false stories had six times the retweeting rate on Twitter as true stories. The researchers did not interpret that finding as specific to Twitter, and the result may be specific to the time of the study, a time when mistrust of conventional media sources was higher than usual. Rather, these authors interpreted their results as confirming that people are “more likely to share novel information.” In other words, contagion reflects the urge to titillate and surprise others. We can add another twist to that conclusion: a new story correcting a false story may not be as contagious as the false story, which means that the false narrative may have a major impact on economic activity long after it is corrected.

Proposition 6: Contagion of Economic Narratives Builds on Opportunities for Repetition

Proposition 7: Narratives Thrive on Attachment: Human Interest, Identity, and Patriotism

Epidemics can be fast or slow, big or small. The timetable and magnitude of epidemics can vary widely. 2.  Important economic narratives may comprise a very small percentage of popular talk. Narratives may be rarely heard and still economically important. 3.  Narrative constellations have more impact than any one narrative. Constellations matter. 4.  The economic impact of narratives may change through time. Changing details matter as narratives evolve over time. 5.  Truth is not enough to stop false narratives. Truth matters, but only if it is in-your-face obvious. 6.  Contagion of economic narratives builds on opportunities for repetition. Reinforcement matters. 7.  Economic narratives thrive on human interest, identity, and patriotism. Human interest, identity, and patriotism matter.

these perennial narratives’ overarching and ever-shifting influence on society today, explaining how many of the challenges that we tend to attribute to discrete contemporary forces are in fact influenced profoundly by narratives—stories that took root generations and even centuries ago but that reappear in newly configured expressions.

Typically, when a narrative reappears, say in another country or a few decades later, the mutated narrative tends to have features different from those of the original narrative—a different celebrity, different visual images, a different punch line.

Mutations in a narrative or in the environment surrounding the narrative may cause it to become an economic narrative by tying it better to economic decisions. A mutation may also occur that increases contagion but twists the story so that it ceases to be the same economic narrative. It may then morph into some different moral or lesson afterward. For example, as we shall see below, a narrative about labor-saving machines replacing jobs (chapter 13) created a sense of fear during the Great Depression of the 1930s, but the same narrative mutated (chapter 14) to create a sense of opportunity during the dot-com boom of the 1990s.

Narratives may be relevant to economic events even if the timing of the narrative’s appearance does not coincide with the event. When it goes epidemic, a narrative may inspire a latent fear, such as a fear that technology will someday replace one’s job, which may result eventually in changes in economic behavior years later when some other narrative or news creates a sense that the feared replacement is imminent.

The first step in our task is organizing and dassifying some of the major economic narratives and the mutations that allowed them to recur over long intervals of time. The remaining chapters in this part describe nine perennial economic narratives, along with some of their mutations and recurrences. Most readers will recognize these narratives in their most recent forms but not in their older forms: 1.  Panic versus confidence 2.  Frugality versus conspicuous consumption 3.  Gold standard versus bimetallism 4.  Labor-saving machines replace many jobs 5.  Automation and artificial intelligence replace almost all jobs 6.  Real estate booms and busts 7.  Stock market bubbles 8.  Boycotts, profiteers, and evil business 9.  The wage-price spiral and evil labor unions

Several classes of confidence narratives have characterized the history of the industrialized economies. The first class is a financial panic narrative that reflects psychologically based stories about banking crises. The second class is a business confidence narrative that attributes slow economic activity not so much to financial crises as to a sort of general pessimism and unwillingness to expand business or to hire. The third is a consumer confidence narrative that attributes slow sales to the fears of individual consumers, whose sudden lack of spending can bring about a recession. Figure 10.1 plots the succession of these narratives since 1800. All of these slow-moving narratives have shown growth paths that span lifetimes. Financial panic came first, followed by narratives about crisis in business confidence, followed by narratives of a crisis in consumer confidence.

increasing self-censorship of narratives may, and sometimes does, encourage panic. Because people are aware that others self-censor, they increasingly try to read between the lines of public pronouncements to determine the “truth.”

In the eighteenth and nineteenth centuries, most people did not save at all, except maybe for some coins hidden under a mattress or in a crack in a wall. In economic terms, the Keynesian marginal propensity to consume out of additional income was close to 100%. That is, most people, except for people with high incomes, spent their entire income. So, to the spinners of narratives of these past centuries, there would have been no point in surveying ordinary people about their consumer confidence. Most people then had no concept of retirement or sending their children to college, so they had no motivation to save toward these goals.3 If they became bedridden in old age, they expected to be cared for by family or by a local church or charity. Life expectancy was short, and medical care was not expensive. People tended to see poverty as a symptom of moral degradation and drunkenness or dipsomania (now called alcoholism), not as a condition related to the strength of the economy. So there was practically no thought that consumer confidence should be bolstered. The people saw the authorities as responsible for instilling moral virtues rather than building consumer confidence. The idea that the poor should be taught to save grew gradually over the nineteenth century, the result of propaganda from the savings bank movement. But contemporary thought was miles away from the idea that a depression might be caused by ordinary people heeding the propaganda and trying to save too much.

Closely related to the idea of crowd psychology is suggestibility, which refers to the idea that individual human behavior is subconsciously imitative of and reactive to others. The word, first seen in the late nineteenth century, appears to be pivotal in narrative constellations and in popular understandings of crowd psychology.

Suggestibility implies that oftentimes we are acting blind or as in a dream. By 1920, the concept of suggestibility was widely known, indicating that people of that era may have felt that other people are easily influenced by abstract or subtle examples, and are therefore more likely to conduct their economic behavior expecting a highly unstable world. The narrative would lead them to expect herdlike behavior and perhaps to contribute to such behavior. If you think that other people are members of an impressionable herd, you may be more likely to try to anticipate the herd’s movements and try to get ahead of them.

Frugality and an impulse to maintain a modest lifestyle have roots going back to ancient times. Sumptuary laws in ancient Greece and Rome, as well as China, Japan, and other countries, forbade excess ostentation. Stories about the disgusting flaunting of wealth are one of the longest-running perennial narratives, in many countries and religions. Opposing these frugality narratives are conspicuous consumption narratives: to succeed in life, one must display one’s success as an indication of achievement and power. The two narratives are at constant war, with modesty relatively strong during some periods and conspicuous consumption dominant at other times. Both are important economic narratives because they affect how people spend or save, and hence they influence the overall state of the economy.

The family is the unit upon which our whole American system of living is built…. Any collapse now of its morale or loss of its solvency will have a disastrous effect on posterity.4 This narrative justified postponing unnecessary expenditures while maintaining an attitude of normalcy, but in doing so it contributed to prolonging the economic depression. It also offered a reason for families not affected by the Depression to avoid conspicuous consumption, in deference to the perceived suffering of other families and the outlook for more of the same.

The Great Depression became a time of reflection about what is important in life beyond spending money. Writing in the United Kingdom in 1931, columnist Winifred Holtby asked: In other words, can we not use this period to get rid of a little snobbery and bunkum and live lives dictated by our own tastes instead of our neighbours’ supposed notions of “what is done”? With so much to do, and a world so rich in experience, must we shut ourselves up into little genteel compartments in which we all adopt the same arbitrary standards, wear the same things, eat the same things, and produce the same sad monotony of “appearances”? … Can we not remember the wisdom of Marie Lloyd’s old song, “It’s a little of what you fancy does you good!”?—not a little of what you fancy your neighbours will fancy that you ought to fancy. Can we not dare to be poor?

The modest economic recovery that started at the bottom of the Great Depression in 1933 occurred, at least in part, because people were spending more because poverty was no longer so chic! All of these narratives imply that the causes and effects of the Great Depression extend beyond economists’ simple story of multiple rounds of expenditure and the effects of interest rates on rational investing behavior.

People seem to have a natural respect for ideas that they perceive as coming from the wisdom of the past and that reflect true or important values.

the new narrative about the gold standard in the 1930s differed from that of earlier years. The difference was partly a matter of new words. Sullivan quotes Talleyrand, Napoleon’s chief diplomat, that “the business of statesmanship is to invent new terms for institutions which under their old names have become odious to the public.”31 The supporters of the devaluation apparently understood this. By the 1930s, the new word devaluation had massively replaced the negative-sounding debasement and inflation. Devaluation refers to a constructive action of enlightened governments, while debasement and inflation connote a moral failing.

The mutation that renewed the old narrative and made it so virulent in 1811 was a new kind of power loom that was eliminating weavers’ jobs. The word Luddite continued to appear regularly in newspapers in following years and today remains a synonym for a person who resists technological progress.

In the depression of 1873–79, a particularly strong depression in the United States and Europe, concern that labor-saving inventions were at least partly to blame for high unemployment took center stage in the popular consciousness, likely worsening the depression. In the United States, this depression is typically attributed to financial speculation leading to the banking panic of 1873, but the fear-inducing narrative about a long-term loss of jobs and job prospects due to labor-saving inventions may help to explain why the depression went global. Certainly the depression of the 1870s was accompanied by farmers’ accelerated adoption of labor-saving machinery, along with more workers destroying machines and hired farm laborers threatening violence.3 Underneath the violence was widespread concern about the outlook for the common laborer.

However, by 1879, a counternarrative had already developed: labor-saving processes will increase the number of jobs, not decrease them. One editorial in the Daily American, dismissing the worries about replacement of labor by machines, noted, The whole tendency of labor-saving processes is towards the elevation of the laboring classes, and if the change is accompanied by some hardship, so is every step in the progress of the human race.8

An 1894 editorial in the Los Angeles Times blamed the severity of the 1890s depression on labor-saving inventions: There is no doubt that the introduction of labor-saving machinery and the consequent increase of production has had more than a little to do with the present depression in business…. It is true that during the past few years the increase in the invention and adoption of labor-saving machinery has been so great that the community has scarcely been able to keep up with it.11

“The reason we have this unemployment is because we are eliminating jobs through labor-saving methods faster than we are creating them.”20 These words, alongside the new official reporting of unemployment statistics, created a contagion of the idea that a new era of technological unemployment had arrived, and the Luddites’ fears were renewed. The earlier agricultural depression, with its associated fears of labor-saving machinery, began to look like a model for an industrial depression to follow.

Underconsumption narratives appeared five times as often in ProQuest News & Newspapers in the 1930s as compared with any other decade. The narrative has virtually disappeared from public discourse, and the topic now appears largely in articles about the history of economic thought. But it is worth considering why it had such a strong hold on the popular imagination during the Great Depression, why the narrative epidemic could recur, and the appropriate mutations or environmental changes that would increase contagion. Today, underconsumption sounds like a bland technical phrase, but it had considerable emotional charge during the Great Depression, as it symbolized a deep injustice and collective folly. At the time, it was mostly a popular theory, not an academic theory.

For example, the US Senate in Washington, DC, replaced its non-dial phones with dial telephones in 1930, the first year of the Great Depression. Three weeks after their installation, Senator Carter Glass introduced a resolution to have them torn out and replaced with the older phones. Noting that operators’ jobs would be lost, he expressed true moral indignation against the new phones: I ask unanimous consent to take from the table Senate resolution 74 directing the sergeant at arms to have these abominable dial telephones taken out on the Senate side … I object to being transformed into one of the employes of the telephone company without compensation.32 His resolution passed, and the dial phones were removed. It is hard to imagine that such a resolution would have passed if the nation had not been experiencing high unemployment. This story fed a contagious economic narrative that helped augment the atmosphere of fear associated with the contraction in aggregate demand during the Great Depression.

Albert Einstein, the world’s most celebrated physicist, believed this narrative in 1933, at the very bottom of the Great Depression, saying the Great Depression was the result of technical progress: According to my conviction it cannot be doubted that the severe economic depression is to be traced back for the most part to internal economic causes; the improvement in the apparatus of production through technical invention and organization has decreased the need for human labor, and thereby caused the elimination of a part of labor from the economic circuit, and thereby caused a progressive decrease in the purchasing power of the consumers.

The same “zero hour” for the labor-saving machinery economic narrative that appeared in 1929 reappeared late in the second half of the twentieth century, but in mutated forms. The term singularity began to be used after Einstein published his general theory of relativity in 1915. The word denotes a situation in which some terms in the equations became infinite, and it was used to describe the astronomical phenomenon of what came to be called the black hole: a “singularity in space-time.” But later the glamorous term singularity came to be defined as the time when machines are finally smarter than people in all dimensions.

This new twist in the fear-of-automation narrative around 1995 did not immediately produce a recession. Most people were not moved to curtail spending because of it, and the world economy boomed. The dominant narratives in the 1990s seemed to be focused on the wonderful business opportunities brought by the coming new millennium. The automation narratives trailed off again in the 2000s, with the distractions of the dot-com boom, the real estate boom, and the world financial crisis of 2007–9. But the automation narratives are still with us, described by new catchphrases.

Recent talk has stressed machine learning, in which computers are designed to learn for themselves rather than be programmed using human intelligence. A Google Trends search for Web searches for machine learning reveals a strong uptrend since 2012, with the Google search index more than quadrupling between 2004 and 2019. The narrative is propelled by recent stories. The highly successful chess computer program AlphaZero is described as working purely through machine learning—that is, without use of any human ideas about how to play chess. This narrative describes a tabula rasa program that plays vast numbers of chess games against itself, given no more information than the rules of the game, and learns from its mistakes.22 In some ways, the machine learning narrative is more troubling than computers running human-generated programs. Historian Yuval Noah Harari describes this narrative as leading toward a “growing fear of irrelevance” of ourselves and worries about falling into a “new useless class.”23 If they grow into a sizable epidemic, such existential fears certainly have the potential to affect economic confidence and thus the economy.

Henry George’s solution to the labor-saving machines problem—and the defining proposal of his book Progress and Poverty, published during the depression of the 1870s—was to impose a single tax on land, to tax away the labor-saving inventions’ benefits to landowners. George’s proposal assumed that the sole purpose of the new machines was to work the land, which might be the case if the economy is purely agricultural. This proposal is analogous to the much-discussed “robot tax” that appeared in public discussion during the Great Depression and has reappeared in the last few years. Taxing companies that use robots, the argument goes, will provide revenue to help the government deal with the unemployment consequences of robotics.25 George proposed to distribute part of the tax proceeds as a “public benefit.”26 His proposal is essentially the same universal basic income proposal that is talked about so often today: In this all would share equally—the weak with the strong, young children and decrepit old men, the maimed, the halt, and the blind, as well as the vigorous.

Traditionally, prices of new homes were widely thought to be dominated by construction costs.6 In fact, it used to be conventional wisdom that home prices closely tracked construction costs. A 1956 National Bureau of Economic Research study noted some short-term movements in US home prices not explained by construction costs between 1890 and 1934, but it concluded: With regard to long-term movements, however, the construction cost index conforms closely to the price index, corrected for depreciation.… For long-term analysis the margin of error involved in using the cost index as an approximation of a price index cannot be great.7 Because their construction cost index included only the prices of wages and materials, but not the price of land, the NBER analysts were viewing investments in homes as nothing more than holdings of depreciating structures, wearing out through time and tending to go out of fashion. With such a narrative, housing bubbles have little chance of getting started.

Social psychologist Leon Festinger described a “social comparison process”10 as a human universal. People everywhere compare themselves with others of similar social rank, paying much less attention to those who are either far above them or far below them on the social ladder. They want a big house so that they can look like a member of the successful crowd that they see regularly. They stretch when they pick the size of their house because they know the narrative that others are stretching. McGinn’s “You Are Where You Live” effect confirms the power of the real estate comparison narrative. As of the early 2000s, when the housing boom was at its peak, there was no other comparable success measure that one could just look up on the Internet.

When a city’s population is expanding, even if the city is not particularly attractive and has no particularly favorable narratives, there will be some people who want to move there. For example, there are always potential immigrants, often from poor or unstable countries, seeking a foothold in advanced countries, and they may choose cities based on arbitrary factors such as proximity to their home country or the existence of a subpopulation speaking their language in the destination city. If land is readily available for purchase there, new houses will be built, and the immigrants’ demand for housing may have minimal impact on prices. But if such land has run out, these immigrants will have to outbid others for existing houses, and home prices will rise. In that case, only the wealthier buyers will be able to live in that city. People who are already living in the city but have no special interest in it have an incentive to sell their houses and take the proceeds to another more affordable house in another city. The supply constraint thus results in higher prices and a wealthier population in that city.

Then, in the early 2000s, during the enormous home price boom, the term flipper became attached to people who bought homes, fixed them up a little or a lot, and sold them quickly. Once again admiring stories were told of their successes. While most people were not enthusiastic enough to actually flip houses, they may have imagined that they were engaged in “long-term flipping” simply by purchasing a primary residence as a long-term investment. Thus they engaged the speculation narrative.

In this present season, on the contrary, conservative opinion has frankly and emphatically expressed the unfavorable view. In a succession of utterances by individual financers [sic] and at bankers’ conferences, the prediction has been publicly made that the end of the speculative infatuation cannot be far off and that an inflated market is riding for a fall.4 Clearly, evidence of speculation was available to the public, which read about it in the news and talked about it on train cars. For example, in the year before its 1929 peak, the US stock market’s actual volatility was relatively low. But the implied volatility, reflecting interest rates and initial margin demanded by brokers on stock market margin loans, was exceptionally high, suggesting that the brokers who offered margin loans were worried about a big decline in the stock market.5 So the evidence of danger was there in 1929 before the market peak, but it was controversial and inconclusive. A high price-earnings ratio for the stock market can predict a higher risk of stock market declines, but it is not like a professional weather forecast that indicates a dangerous storm is coming in a matter of hours. Most people will heed that kind of storm warning. However, in 1929 a great many people did not heed the warning communicated by the high price-earnings ratio. After the crash, many of them must have remembered the warnings and wondered why they had not listened.

The 1987 epidemic draws much of its strength from memories of 1929. Suicides were attributed to the 1987 crash too, but these stories do not seem to have formed long-term memories, for a strong narrative did not develop and there was no reinforcing story of depression after 1987. A 50% margin requirement in force in 1987, but not in 1929, meant that in the United States many fewer people were “wiped out” or “ruined” by the 1987 crash than by the 1929 crash.

Policymakers might take a lesson from both the real estate bubble narratives and the stock market crash narratives: during economic inflections, there is real analytical value to looking beyond the headlines and statistics. We should also consider that certain stories that recur with mutations play a significant role in our lives. Stories and legends from the past are scripts for the next boom or crash.

Anger at business varies through time. People may start thinking business is evil when prices of consumer goods increase substantially. Narratives blame business aggressiveness for rising prices, and public anger may continue after the inflation stops, if the public believes that prices are still too high. Anger can also become inflamed when businesses cut wages. Such anger may induce organized boycotts or disorganized decisions to postpone spending until prices are lower. In such cases, people view their buying decisions in moral terms, not just as satisfying their wants.

the boycott narrative and others in its constellation tend to recur when there is a broad-based undercurrent of social opprobrium, and they are economically important because they affect people’s willingness to spend and willingness to compromise.

By the middle of the depression of the 1890s, the narrative began to change, and the public was becoming fed up with a constant succession of boycotts. The moral authority of boycotts disappears when most people begin to express suspicion and annoyance with them. As Wolman notes: The influence of the American Federation of Labor has been exerted in inducing in its members a greater conservatism in the employment of the boycott. Practically the great majority of its legislative acts from 1893 to 1908 have been designed to control the too frequent use of the boycott. At the convention of 1894 the executive council remarked “the impracticability of indorsement of too many applications of this sort. There is too much diffusion of effort which fails to accomplish the best results.” Thereafter, every few years saw the adoption of new rules restricting the endorsement of boycotts.

After World War I, with immediate postwar inflation totaling 100%, a deflation narrative developed by 1920. The story that consumer prices would fall dramatically was strongly contagious owing to its association with the profiteer narrative. Indeed, during the 1920–21 depression, thousands of newspaper articles noted that certain individual prices had fallen to their prewar 1913 or 1914 levels. The newspapers’ writers and editors knew that readers would respond well to such stories because, to most people, it seemed natural that once the war was over, prices would return to their old levels: a very important perceived “return to normalcy” that might eventually encourage consumers to buy a new house or a new car, but only after prices came down fully.

As one observer wrote in 1920: The buying public knows that the war is over and has reached the point where it refuses to pay war prices for articles. Goods do not move, for people simply will not buy.6 Populist anger grew, along with protests against profiteering manufacturers and retailers. The protests sought to take advantage of a basic economic principle: If people determine to buy foodstuffs or anything else only what they actually cannot do without, the working of the inexorable law of supply and demand will operate automatically to bring conditions to a more normal state.7 Thus thrift became a new virtue as people waited for the return of the “normal” prices of 1913.

The economic narrative of the 1920s created an emotionally rich atmosphere of expectations about falling prices. The narrative was not only that it was smart to postpone purchases, but also that it was moral and responsible to do so.

The profiteer narratives did not stop with the end of the war in 1918. During the postwar inflation, in 1920 and 1921, narratives spread of customers angry at high prices chastising their milkman and telling their butcher they would stop eating meat altogether to spite them. Economists understood why wartime inflation continued until 1920 (heavily indebted governments faced troubles from a war-disrupted economy and did not want to raise taxes or raise interest rates, which would add to their deficit), but the public at large did not. The public began to view the wartime experience and the immediate postwar experience in terms of a battle between good and evil. The popular author Henry Hazlitt wrote in 1920: Hence we have self-righteous individuals on every corner denouncing the outrages and robberies committed by a sordid world. The butcher is amazed at the profiteering of the man who sells him shoes; the shoe salesman is astounded at the effrontery of the theatre ticket speculator; the theatre ticket speculator is staggered at the highhandedness of his landlord; the landlord raises his hands to high heaven at the demands of his coal man, and the coal man collapses at the prices of the butcher.13 We might ask: Did these people deserve to be called profiteers? It seems that their only crime was selling at higher prices in an inflationary period.

In 1917, during World War I, the United States imposed a 60% excess profits tax on profits above the prewar 1911–13 level. The excess profits tax was not revoked until October 1921, because anger at corporations lingered long after the war was over. The tax contributed to the 1920–21 depression by encouraging companies to postpone profits until after the tax was revoked. Meanwhile, people held off buying, not only because of their anger at selfish profiteers but also because of the perceived opportunity to profit from postponing their purchases during a time of falling prices.

Many of the narratives surrounding the recession of 1973–75 had a source in human anger. The most cited cause of this recession—the oil crisis generated by OPEC angrily protesting US support of Israel in the 1973 Yom Kippur War—was only part of the story. The price of oil suddenly quadrupled to unheard-of levels, generating anger among consumers and stories of difficulties dealing with oil rationing in the United States, such as odd-even rationing of gasoline. (Consumers could buy gasoline only on odd-numbered days if their license plate ended with an odd number, and only on even-numbered days if their license plate ended with an even number.) Higher oil prices caused higher electric bills, and anger at the perceived injustice was one of the reasons many people started keeping much of their homes in darkness, as a sort of protest.37 In the period of runaway US inflation of the 1970s, when many viewed inflation as the nation’s most important problem, one observer wrote in July 1974, “Fighting inflation is like fighting a forest fire, it requires courage, team play, and coordinated sacrifice.”38 At the time, US annual inflation was 12%, which was a record high excluding periods surrounding the world wars.

The wage-price spiral narrative took hold in the United States and many other countries around the middle of the twentieth century. It described a labor movement, led by strong labor unions, demanding higher wages for themselves, which management accommodates without losing profits by pushing up the prices of final goods sold to consumers. Labor then uses the higher prices to justify even higher wage demands, and the process repeats itself again and again, leading to out-of-control inflation. The blame for inflation thus falls on both labor and management, and some may blame the monetary authority, which tolerates the inflation. This narrative is associated with the term cost-push inflation, where cost refers to the cost of labor and inputs to production. It contrasts with a different popular narrative, demand-pull inflation, a theory that blames inflation on consumers who demand more goods than can be produced.

In contrast to the 1920s and the preceding chapter, there were now multiple possible sources of evil behind inflation, not so focused on evil businesses of various kinds, but now also on evil labor.

Social media and search engines have the potential to alter the fundamentals of contagion. In the past, ideas spread in a random, non-systematic way. Social media platforms make it possible for like-minded people with extremist views to find each other and further reinforce their unusual beliefs. Contagion is not slowed down by fact-checkers. In contrast, the Internet and social media allow ideas to be spread with central control that is nonetheless poorly visible. Designers of social media and search engines have the ability to alter the nature of contagion, and society is increasingly demanding that they do so to prevent devious use of the Internet and the spread of fake news.

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Key Points from Book: Unknown Market Wizards: The Best Traders You’ve Never Heard Of

Jack D. Schwager is probably one of the best author on trading books that have ever been written. His first book, Market Wizards, published in 1989 has attained a cult status among market participants and helped them develop an edge and strategy that are important in money management business. In his latest book released this year, he interviewed 11 traders (6 futures and 5 stocks) and share the insights he gathered from the conversation. Below are the points made in the book, which do not do justice compared to reading the book itself, but serves as a personal reminder for me in the years ahead.

Every decade has its characteristic folly, but the basic cause is the same: people persist in believing that what has happened in the recent past will go on happening into the indefinite future, even while the ground is shifting under their feet. —George J. Church

While he was buying into weakness, he wouldn’t just put on a full position and hold it. He would probe the market for a low. He would get out of any trade that had a loss at the end of the week and then try again the next time he thought the timing was right. He kept probing, probing, probing.

“There are two parts to a trade: direction and timing. And, if you’re wrong about either one, you’re wrong on the trade.”

Yes, I saw that he took much smaller positions than he could. The lesson I learned from Dan was that if you could protect your capital, you would always have another shot. But you had to protect your pile of chips.

So it wouldn’t bother you going long at $1.50 after getting stopped out twice at $1.20? No, that has never bothered me. I think that type of thinking is a trap that people fall into. I trade price change; I don’t trade price level.

it is easy to believe in a trade that conforms to conventional wisdom. It used to bother me to be wrong on a trade. I would take it personally. Whereas now, I take pride in the fact that I can be wrong 10 times in a row. I understand that my edge comes from the fact that I have become so good at taking losses.

Nor does it make any sense to me that some people use their open profits on the trade to add more contracts. That, to me, is the most asinine trading idea I ever heard. If you do that, you can be right on the trade and still lose money. In my own trading, my positions only get smaller. My biggest position is the day I put a trade on.

Charts are wonderful in finding specific spots for asymmetric risk/reward trades. That’s it. I am focused on the probability of being able to get out of the trade at breakeven or better rather than on the probability of getting an anticipated price move.

I think there are things that the winners have in common. They respect risk. They limit their risk on a trade. They don’t automatically assume they will be right on a trade. If anything, they assume they will be wrong. They don’t get too excited about a winning trade or too bummed out about a losing trade.

They risk way too much. They don’t have a methodology. They chase markets. They have a fear of missing out. They can’t keep their emotions in check; they have wild swings between excitement and depression.

The essence of Brandt’s strategy is to risk very little on any given trade and to restrict trades to those he believes offer a reasonable potential for an objective that is three to four times the magnitude of his risk. He essentially uses charts to identify points at which it is possible to define a close protective stop that is also meaningful—points at which a relatively small price move would be sufficient to trigger a meaningful signal that the trade is wrong.

mathematically, by increasing position size, a method with higher return-to-risk can always be made to yield a higher return at the same risk level than a lower return-to-risk method (even if its return is higher).

Success in trading one’s own account will not necessarily translate to success in managing money. Some traders may be comfortable and do well trading their own money but may see their performance fall apart when trading other people’s money. This phenomenon can occur because, for some traders, a sense of guilt in losing other people’s money may impair their normal trading decision process.

If your portfolio is sailing to new highs almost daily and virtually all your trades are working, watch out! These are the times to guard against complacency and to be extra vigilant.

Brandt’s motto is: Strong opinions, weakly held. Have a strong reason for taking a trade, but once you are in a trade, be quick to exit if it doesn’t behave as expected.

It was like Jesse Livermore used to say, “You make your money in the sitting.”

We used to have a saying in Hong Kong, “Should’ve been up, but it’s down, so short it; should’ve been down, but it’s up, so long it.” That trading philosophy became the basis of what I wanted to do: When the tape is telling you something, don’t fight it; go with it.

The low of the reversal day will be my stop. I’m not going to argue with it. If I go long, and the market goes back to the low of that day, I’m out. I’m so disciplined with this stuff. It’s not just that I have a stop on every trade, which I do, but that I have a stop that is based on some meaningful market move. The news came out, the market gapped down, and then it closed up. OK, that low is going to be my stop forever. If that low is hit, I’m out.

By definition, everyone can’t make outsize returns. So if everyone is doing something, the only way to make outsize returns is by being on the other side. The great thing about the markets is that I can wait until there is a confirmation before taking the opposite position.

People fail, and they quit; they get scared. For some reason, I have a risk instinct. I hate failing, but I don’t mind taking the risk and then failing.

Markets bottom on bearish news and top on bullish news.

I learned that keeping losses as small as possible is critical to capital preservation. The most crucial thing in trading is mental capital. You need to be in the right headspace for the next trade. I find that when I go into a deep drawdown, my mindset is not right. I might start forcing trades to try to make money back. I might get gun-shy about taking the next trade.

When Brandt gets into a trade, he expects it to work straightaway if he is right. The best trades just go. If there is any sign that the market isn’t doing that, he tightens his stop for getting out. That approach fits the way I trade the fundamentals.

sometimes, when I did get out, the trade would then go to the target. When that happens, it teaches you to do the wrong thing, which is to hold on. The problem is that you only remember the times you got out, and the trade then went to the target; you don’t remember all the times when you got out, and it saved you money.

If there is an explosive upmove, I will tend to take profits because any meaningful stop would risk giving back too much of the open profits. If, however, the market has a steady trend, I will move my stop up gradually.

To be a good trader, you have to have a high degree of self-awareness. You have to be able to see your flaws and strengths and deal effectively with both—leveraging your strengths and guarding against your weaknesses. It doesn’t matter if I miss a trade because there will always be another opportunity. Mental capital is the most critical aspect of trading. What matters most is how you respond when you make a mistake, miss a trade, or take a significant loss. If you respond poorly, you will just make more mistakes. If you take a trade that results in a loss, but you didn’t make a mistake, you have to be able to say, “I would take that trade again.” Opportunities are dispersed. You might have an opportunity today and then have to wait three months for the next opportunity. That reality is hard to accept because you want to make a steady income from trading, but it doesn’t work that way. In 2017, nearly all my profits came from two weeks in June and one day in December. That’s it. The rest of the year amounted to nothing. Have a long-term focus and try to increase your capital gradually rather than all at once. You have to forgive yourself for making a mistake. For a long time, I would beat myself up anytime I made a mistake, which only made things worse. You have to accept that you’re human and will make mistakes. It took me four or five years to understand that. I don’t know why it took me so long. Staring at these screens all day long is like a casino inviting you to click. You have to guard against the temptation of taking impulsive trades. If a bad or missed trade destabilizes me, I have rules for bouncing back: Take some time off, exercise, go out in nature, have fun.

I realized that I was in a position, hoping for it to work. The second I realized that I was hoping and not trading anymore, I immediately liquidated everything.

the big trades are pretty simple. You don’t have to go looking for them, but you do have to wait for them. Trading opportunities in the market ebb and flow. There will be periods in the markets where opportunities dry up, and there will be nothing to do. In those nothing periods, if you are looking for something to do, that is when you can create real damage to your account.

A lot of losing traders I have known thought they had to make money consistently. They had a paycheck mentality; they felt they had to make a certain amount every month. The reality is that you may go through long periods when you don’t make anything, or even have a drawdown, and then have a substantial gain. Entrepreneurs understand that. They will invest in a company for a long time, and the payoff comes in one hit after many years of hard work. If you are looking for outsize profits, you can’t approach that goal with a mindset of consistency.

Successful traders take care of the downside and know that the upside will take care of itself.

The trades that Sall has the patience to wait for have two essential characteristics: They are trades he perceives have a high probability of moving in the anticipated direction. They are asymmetric trades: the potential gain far exceeds the risk taken.

If you ever find yourself in a trade based on hope, get out. You need conviction, not hope, to stay in a trade.

“I don’t have to be right all the time; I just need to be right in a big way a few times a year.”

I realized an unexpected event that ran counter to the news flow was present in every one of my big winning trades. Another characteristic of these trades was that my reason for entering was very clear; I didn’t confuse my short-term and long-term views. I also noticed that these trades were never down by much and usually tended to be profitable almost immediately, whereas the trades that didn’t work tended to go offside quickly and stay offside.

Always make sure your stops are set at a point that disproves your market hypothesis; never use a monetary stop—a stop point selected because it is the amount of money you’re willing to risk. If you are tempted to use a monetary stop, it is a sure sign that your position size is too large.

placing large bets when you had the right set up, and keeping bets small when you didn’t. My winning percentage on trades is way less than 50%, but I still do well because I can recognize the one or two times a year when all the pieces of the puzzle are in place, and I need to bet big on a trade.

How would you define your trading methodology? I look at trading like a puzzle; I have to get the four corners in first. What are the four corners? The first corner is technical analysis; you have to have the right chart pattern. The second corner is a clean share structure. What do you mean by that? The stock has few or no options or warrants, and preferably, there are fewer than 200 million shares. What are the other two corners? Being in the right sector and having a catalyst or story that will make the stock or sector move up. Once the four corners are in place, you can then fill in the pieces.

When you see a big movement in a stock price, there is a reason why that price change happened. In many cases, the price moved because there is some inflection point in demand for that company’s services or products. Was there a way to identify that change early? I knew those opportunities existed, but I couldn’t figure out how to capture more of them. The opportunities I was catching were very random and based on my physicality—where I was, and what I saw at that moment in time.

Don’t ever listen to anybody when you are in a position. Stick to your own approach and avoid being influenced by contradictory opinions.

One of the toughest dilemmas that face systematic traders is deciding whether an ongoing losing period for a system represents a temporary phase that will be followed by a recovery to new equity highs, or whether the system no longer works. There is no simple prescription for how to decide between these two opposite interpretations. However, the lesson systematic traders should draw from this chapter is that sometimes abandoning a system is the right decision. It is one of those rare instances where discipline in trading—in this case, following the system absolutely—may not be a good thing.

Any system—repeat, any system—can be made to be very profitable through optimization (that is in regards to past performance). If you ever find a system that can’t be optimized to show good profits in the past, congratulations, you have just discovered a money machine (by doing the opposite, unless transaction costs are excessive). Therefore, incredible past performance for a system that has been optimized may be nice to look at, but it doesn’t mean very much. Optimization will always, repeat always, overstate the potential future performance of a system—usually by a wide margin (say, three trailer trucks’ worth). Therefore, optimized results should never, repeat never, be used to evaluate a system’s merit. For many, if not most systems, optimization will improve future performance only marginally, if at all. If optimization has any value, it is usually in defining the broad boundaries for the ranges from which parameter values in the system should be chosen. Fine-tuning optimization is, at best, a waste of time and, at worst, self-delusion. Given the above considerations, sophisticated and complex optimization procedures are a waste of time. The simplest optimization procedure will provide as much meaningful information (assuming that there is any useful information to be derived).

“Michael is unique because he combines two very different approaches: long equities on one side and a unique short strategy on the other. His ability to do both shows how adaptable he is as a person, and adaptability is critical in the game of speculation.”

In a recession, the market will typically go down 20%–30%. Assuming my 60% long portfolio does no better than the market, it will lose approximately 12%–18%. I would expect my short-term trading and short positions to cover that loss.

Appropriate risk management encompasses two tiers: the individual trade level—limiting the loss on any single trade—and the portfolio level. At the portfolio level, there are again two components. First, analogous to individual trades, there are rules to limit the loss for the portfolio as a whole. Such rules might include a defined process for reducing exposure as a loss drawdown deepens, or a specified percentage loss at which trading is halted. The second element of risk management at the portfolio level pertains to the portfolio composition. Positions that are highly correlated would be limited to the extent feasible. Ideally, the portfolio would include positions that are uncorrelated and, even better, inversely correlated with each other.

It is commonplace for traders to get sloppy when they are doing particularly well. Beware of letting a period of strong performance go to your head.

I knew I wanted to find something where the success or failure would depend only on me, not my colleagues, my boss, or anybody else. If I make money, that’s great; if I lose money, it’s my mistake. Trading is great in this way. You can’t find many other activities where success or failure depends only on you.

Dhaliwal makes the critical point that protective stops should be placed at a level that disproves your trade hypothesis. Don’t determine the stop by what you are willing to lose. If a meaningful stop point implies too much risk, it means that your position is too large. Reduce the position size so that you can place the stop at a price the market shouldn’t go to if your trade idea is correct, while still restricting the implied loss at that stop point to an amount within your risk tolerance on the trade.

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Key Points from Book: The Great Demographic Reversal

The Great Demographic Reversal: Ageing Societies, Waning Inequality, and an Inflation Revival

Charles Goodhart, Manoj Pradhan

The summary below is key points from an excellent book discussing the outlook and challenges the global economy will face in the coming decades. This book is very well written, soundly research, and worth reading for all economic enthusiasts, strategists, and politicians to understand the global context of inflation and social factor that will impact asset prices over the long-term.

The increase in the working age population (WAP, aged 15–64) in China outstripped the combined increase in Europe and the USA from 1990 to 2017 over fourfold—China saw an increase of over 240 million while in the latter two WAP increased by less than 60 million and mostly in the USA. The participation of the working age population also tilted the scales heavily in China’s favour.

These two politico-economic developments, the rise of China, and the return of Eastern Europe to the world trading system, provided an enormous positive supply shock to the available labour force in the world’s trading system.

As a result, globalisation surged ahead, with trade flows over the years 1990 until 2017 growing by 5.6% per annum, compared to the growth of world GDP of 2.8%. In 2004, the share of world manufacturing output produced by China was 8.7%; by 2017, it had reached 26.6%.

Combining these two factors, the rise of China, globalisation and the reincorporation of Eastern Europe into the world trading system, together with the demographic forces, the arrival of the baby boomers into the labour force and the improvement in the dependency ratio, together with greater women’s employment, produced the largest ever, massive positive labour supply shock.

When such a positive supply shock to labour occurs, the inevitable result is a weakening in the bargaining power of the labour force. Especially in advanced countries, a fall in real wages has seen the economic position of unskilled labour as well as semi-skilled labour suffer relative to capital, profits and managerial and skilled labour remuneration.

No wonder that the deflationary forces have been so strong. During these 28 years, prices of durable manufacturing goods tended to fall regularly in most advanced economies, though perhaps slightly less so in more recent years. In contrast, services inflation in developed market economies, having initially fallen quite sharply in the 1980s, tended to stabilise from the 1990s onwards at nearer 2%,

First, the declining growth rate of the labour force will necessarily reduce the growth of real output, unless there is an unexpected and quite remarkable surge in productivity. Growth rates generally cannot be expected to recover, if at all, beyond the disappointingly slow levels of the years since the GFC (Chapter 3). Second, our highest conviction view is that the world will increasingly shift from a deflationary bias to one in which there is a major inflationary bias (Chapter 5). Why? Put simply, improvements in the dependency ratio are deflationary, since workers produce more than they consume (otherwise it would not be profitable to employ them in the first place), while dependents consume but do not produce. The sharp worsening in the dependency ratios around the world means that dependents who consume but do not produce will outweigh the deflationary workers. The inevitable result will be inflation. With the supply of labour shifting down, standard economics suggests that their bargaining power will increase, and that real wages and the relative income share of labour will start rising again. While this will have beneficial effects on inequality within countries, it will be inflationary as unit costs rise. Add on top of this an increasing tax burden on workers (which we explain below), and they may well raise their wages demands in order to secure a desired real wage after taxation.

Third, real, inflation-adjusted interest rates, particularly at the longer end of the yield curve, may rise (Chapter 6) because of the behaviour of ex-ante (expected) savings and investment. That the elderly will dissave is not controversial. Those who believe real interest rates are likely to fall or stay low clearly believe that investment will fall even further below savings—we disagree. There are (at least) two reasons to believe that investment will remain more buoyant than many believe. First, the demand for housing will remain relatively steady as the elderly stay in their houses and new households create demand for new construction. Second, the corporate sector is likely to invest in capital in a way that raises the capital-labour ratio, in order to boost productivity. In net terms, we believe savings are likely to fall by more than investment,

In effect, we are in a debt trap. Debt ratios are so high that increases in interest rates, especially at a time in low growth, may drive exposed borrowers into an unsustainable state. As a result, the monetary authorities cannot raise interest rates, either sharply or quickly, without running into the danger of provoking another recession, which itself would make everything worse. But that will leave interest rates, and the accompany flood of liquidity, sufficiently expansionary (accommodating, in Central Bank speak) that debt ratios are likely to increase even further.

Global capital was largely prevented from accessing China’s financial markets, while the early returns from China’s financial markets were not attractive enough for overseas investment to chase. As a result, global capital flowed into physical investment. Strict capital controls allowed China to maintain a competitive global advantage. That same strategy allowed financial repression to be pursued at home in order to direct the domestic pool of saving towards state-owned enterprises (SOEs) with government-owned banks as the conduit.

Pierce and Schott (2012) document the ‘surprisingly swift decline in US manufacturing employment’ over the 2000s (see Diagram 2.2), identifying the removal of the threat of future tariffs against China as the key driver of that decline. That changed, according to the authors, when the US Congress approved a Permanent Normal Trading Relations (the US equivalent of the MFN) status on China in 2000 and eliminated the threat of tariffs in the future. Without a friction like tariffs to justify the onshore presence of manufacturing jobs, the production of many goods left US shores for China.

manufactured goods are impossible to differentiate by their geographical origin. Regardless of where they are manufactured, these goods are usually tradeable, must match a global standard of quality, and must be cost-efficient relative to producers in other regions. If an economy manages to enlarge the share of the manufacturing sector in its economy at an early stage of its development, its labour productivity will converge with global standards faster and consistently.

Nabar (2011, IMF) finds a negative correlation between urban savings and the decline in real deposit rates. When banks fail to protect household savings, households tend to save more, not less, in order to achieve a ‘target’, whether that is for education or the purchase of a home. China’s household savings have also been linked to the lack of a social safety net, and importantly in the context of this book, to the life cycle of a population that is saving for retirement.

The excess of desired savings over desired investment in the AEs was driving the equilibrium real interest rate lower by itself, since the dependency ratio improved in the 1980s. The injection of excess savings from China and North Asia served to push interest rates even lower.

China’s working age population has been shrinking (Diagram 2.5), a reflection of its rapidly ageing population. The internal migration that had provided a seemingly endless supply of labour to the industrial zones has reached the ‘Lewis turning point’, a point at which the surplus rural labour supply no longer provides a net economic benefit through migration

On the capital side, China’s phase of rapid capital accumulation in the sectors that are connected to the global manufacturing supply chain has already drawn to a close. The collapse in the manufacturing complex and the property sector back in 2014–2015 has been followed by consolidation and capacity cutting rather than in more capital accumulation.

even though a substantial portion of the credit extended by state-owned banks to SOEs was linked to excess capacity, both banks and SOEs were reluctant to write them off. Even if banks could be recapitalised by the government, SOEs that wrote down substantial loans would not receive any further funding, and would probably have to lay off a substantial part of the workforce. Instead, banks ‘evergreened’ the loans granted to SOEs and allowed them to stay operational. The presence of ‘zombie’ firms in China is therefore at least partly a function of societal and political constraints. Instead of mass layoffs, workers voluntarily left for jobs in urban areas in the gig economy, or were let go in small numbers when SOEs merged.

As the labour force and population growth go into reverse, overall growth will slow down. Household savings are likely to fall, with consumption directed to ageing- and health-related services, in the absence of a full and proper social safety net. This may happen either directly, or indirectly via the government.

Japan’s government debt cannot be cancelled even if it is domestically held. Why? Because the ‘leakage’ via households is too big. The enormous stock of government debt is held almost entirely domestically, with a huge chunk held by pension funds. Let’s say Japan’s government decides to cancel its debt. That would impair the asset side of the balance sheet of Japan’s pension funds, which would make it virtually impossible for them to service the liabilities that are due to the household sector. Shocked by the loss of future retirement income, households would raise their savings immediately and the ‘paradox of thrift’ would then lead to a consumption collapse and push Japan into a depression. Thus, the ‘leakage’ of Japan’s debt cancellation via household spending is just far too big.

In China, both sides of leverage are on the same balance sheet (i.e. the government’s) since so much of the corporate debt in question was issued by state-owned banks to SOEs. The ‘leakage’ (i.e. labour employed by SOEs) is smaller. China’s state-owned banks have issued debt to domestic state-owned enterprises. If banks were to cancel the debt, it would create two issues. First, banks would have to take a hit to their capital in a discrete step—we will address this point just below. Second, it would be difficult to lend any more to SOEs. The SOEs would have to cut down operations and possibly fire a substantial part of their workforce. That’s the problem—this could lead to social unrest which the authorities do not want.

Once the ranks of labour in the SOEs thin out (a process that is already underway but will still take years to reach critical mass), cancelling the debt will cause far fewer negative spillovers.

the price for accruing a large stock of debt will be paid via a shortage of credit for consumption and for businesses in the services sector. Even though debt-equity swaps allow for a much smoother deleveraging, they will eat up bank capital as the value of swapped equity is slowly written down to match the realised value of the non-performing assets that had been financed by the debt in the first place. While bank capital is being eaten away, and while real wage growth finds lower support from capital accumulation, the ability and willingness of banks to lend will be lower. Thus, even though consumers and the private sector account for an increasingly larger share of the economy, they will not be able to draw upon future earnings to consume or grow faster today. In summary, the future holds a much weaker path for the Chinese consumer, but a stronger rebound in productivity (particularly for the SOEs that work aggressively to reduce non-performing assets and loans ). Debt run-off is likely to reinforce these trends—it will not create a crisis, but it will constrain the flow of credit in the future as banks digest the excesses of the past.

For China, the implications of everything we have discussed are threefold. First, China will no longer be a global disinflationary force. If anything, demographic pressures and the Lewis turning point imply that inflationary pressures, with which the economy has never had to deal until now, could materialise and catch us off-guard. Second, falling savings related to the ageing population and to the end of financial repression will push the current account into deficit. The capital account, as we discussed earlier, could push the current account back into surplus, but it is not clear how the contrasting flows in household wealth, financing of the Belt and Road Initiative and foreign investment within China will play out in net terms. Without the persistent ‘uphill’ flows of capital that China’s earlier current account surplus had engendered, USA and global bond yields (and in turn asset prices) will see a reversal of the support from this source they had in the past. Third, China’s ability to introduce labour-saving and productivity-enhancing technology depends on the not-inconsiderable innovations that can be generated at home. Without the help of foreign firms transferring technology, and with political sensitivities around the acquisition of foreign technology firms by Chinese firms, an organic improvement in technology will be more difficult.

The danger facing the global economy is precisely that the economies that have dominated global growth are facing the biggest demographic challenges. And that means even if the world as a whole still faces substantial population growth going forward, the economies that shaped global growth for the last 35 years are the ones which bear the brunt of the demographic headwinds . Put differently, if demography is to leave only a scar on the global economy, then the economies that have done very little for global growth in the past few decades are the ones that must do much, much more in the future. Seen from this perspective, it should be clear that the disruption from technology that many fear so much in the advanced economies is actually an imperative without which the damage to global growth could well be far worse.

Indeed, many have explained part of the slower recent slowing of productivity gains as due to the ageing of the ‘baby-boom’ cohort. But as discussed in greater length in Chapter 10, this view is being reconsidered. Even if productivity does not, in fact, suffer from higher participation of the elderly, there could be a problem over promotion. If we pressurise the old to continue in work, will it cause blockages for the young? Next, a comfortable retirement is perceived as part of inter-generational equity and fairness. Finally, the old have typically had a much higher propensity to vote than the young. It is politically risky to seek to remove benefits from the old that they had perceived as their just reward.

The harder it becomes to find and hire qualified workers, the more employers will be forced to raise the productivity of those that they can attract and maintain in order to remain competitive;

Next, some of the sluggish growth of productivity per worker in the last few decades may be due to a combination of technology, shifting jobs from semi-skilled to unskilled, as discussed subsequently in Chapter 7 on ‘Inequality and the Rise of Populism’, and, perhaps, the growing participation of older workers.

Finally, an ageing population tends to consume services, such as care and medicine, where productivity increases are harder to obtain than in manufacturing,

The basic problem is that ageing is going to require increasing amounts of labour to be redirected towards elderly care at exactly the time that the labour force starts shrinking.

In the old days, people were mostly free of dependency cares from around 40 until retirement, when their earnings were highest and the prospect of retirement coming into sight. That encouraged saving. Nowadays people will be most free of dependency caring in their 20s, when earnings are lower and the prospect of living until 90+ almost unimaginable. Not a good time for saving. Then from 30 onwards, in some cases continuously, until your own retirement, looking after your own children12 would be followed in short order by the need to help look after your parents. See Bauer and Sousa-Poza (2015, 2019). Looking after dependents, whether children or elderly parents, takes time, effort and money. Given a combination of immediate emotional ties and time discount, we tend to believe that support for dependents will take precedence over saving for an uncertain future. In other words, we see the changing life cycle as a force likely to reduce the personal sector savings ratio and to throw a yet higher fiscal burden on the public sector to provide acceptable standards of living and health care for the elderly.

Inflation is the outcome of several interacting forces. These include underlying structural trends, demography and globalisation, and the macro-economic balance between savings and investment, as well as purely monetary phenomena.

If we are right in our political economy assumption that the social safety net will remain in place, then the age profile of consumption will continue to be flat or even upward sloping. The elderly will depend on (and vote for) government support and continue to save too little for the longer life they have inherited. The ineluctable conclusion is that tax rates on workers will have to rise markedly in order to generate transfers from workers to the elderly. Workers, however, would not be helpless bystanders. Labour scarcity in AEs (and some EMEs) will put them in a stronger bargaining position, reversing decades of stagnation in AEs. They will use that position to bargain for higher wages. This is a recipe for recrudescence of inflationary pressures.

The renewal of upwards pressures on inflation stems from three interacting and interlocking viewpoints: An intuitive balance based on the dependency ratio; An exercise based on labour market demand and supply, otherwise known as the Phillips curve; and A consideration of the relative balance of savings and investment in the (non-financial) private sector, and the effects of this on the public sector, and its policies.

Almost by definition, an improvement in the overall participation rate is deflationary, as workers outstrip those who do not work. As the dependency ratio falls, the disinflation from more workers overwhelms the inflationary impact of dependents. By the same token, a rise in the dependency ratio will be inflationary (too many mouths chasing too little food).

Dependents (the young and the old) are purely consumers and hence generate an inflationary impulse, whereas workers can offset this inflationary impulse through production.

switching from a deficit to a surplus when age-related expenditures are going to skyrocket will be extremely painful, and we think not politically feasible. Inflation then becomes a way that macroeconomic balance is restored.

Ben Bernanke (2005) famously attributed the declining real rate of interest from the 1990s onwards to a ‘savings glut’. This was down to two drivers: first, baby boomers saving for their future retirement and, second, by the ageing, but increasingly prosperous workers in Asia (especially in China) saving for their old age due to an inadequate social safety net. The result was that household savings ratios were high. But as the baby boomers retired, and the ratio of the old (individuals who were dissaving) to workers (who were saving) rose, the household saving ratio started declining. We plot the personal sector savings ratio against the dependency ratio for a selection of countries below, indicating that as the dependency ratio worsens (i.e. rises), the household savings rate falls (Diagram 5.1). Diagram 5.1 The household savings rate falls as the dependency ratio worsens (Source OECD)

From the point of view of the young, staying at home means they can save on rent and other amenities. But they need to allocate those savings for the future use, e.g. for housing down payment, or indirectly by building up human capital. From the point of view of parents, these social changes will then be reducing the prime period of saving for retirement by a large chunk (reducing that period from, say, 45–65 to 52–67).

If so, then that would lead to higher profit margins, a greater share of profits in national income than otherwise, and lower investment. In a recent NBER Working Paper, Liu et al. (January 2019) have argued that the continuation of very low interest rates has itself led to greater market concentration, reduced dynamism and slower productivity growth.

The development of software, for example, requires a lot of human skills and effort, but relatively little fixed investment. Insofar as technology is shifting the balance towards human capital and away from fixed investment, the ratio of expenditures on fixed capital to total revenues and output is likely to decline, possibly quite sharply.

In any case, we claim that growing labour market tightness will raise wages and unit labour costs. For such reasons, we think it quite likely, though far from certain that, in future, investment per worker will rise.

Their main conclusions, with which we agree, are: overall growth and total hours worked will slow down as ageing advances (which we can see because β3—which represents the coefficient on the aged profile of the population—is negative for growth and even more so for total hours worked); both the proportion of young and old are inflationary for the economy—this can be seen clearly by the coefficients for inflation of β1 and β3; and both the investment and the personal savings ratios fall thanks to demographics—as seen by a negative value for β3 for both investment and the personal savings ratios.

Slower population growth will lower savings (assuming a constant dependency ratio), but will equally lessen the need for more capital, houses, equipment, etc. However, this doesn’t tell us whether the capital/labour ratio will fall or rise, thereby raising or lowering the marginal productivity of capital. With both ex ante S and ex ante I moving in the same direction, assessing the likely balance between the two becomes problematic. The behaviour of household savings according to the life-cycle hypothesis in the presence of a social safety net and the impact of ageing on China’s savings explain why savings will fall.

Almost inevitably, health expenditures will rise further (Diagram 6.2), while the retirement age simply hasn’t kept up with longevity. Both health expenditures and expenditures on public pension transfers (Diagram 6.3) will continue to rise along with the ageing of AE societies. So far, measures to enforce participation in the labour force by raising the retirement age have not materialised, except in a handful of places which have enforced a modest increase in retirement age. Longevity, on the other hand, has gone up significantly thanks to medical advances and might go up further if the science of ageing makes rapid advances. As a result, the gap between longevity and the retirement age has been increasing in line with increases in longevity,

The economic impact of China on the world economy has been great. One dimension of this has been to impart upward pressure on the price of raw materials including, notably, oil. Much oil has been produced in relatively sparsely populated countries (Saudi Arabia and the Gulf and Norway). With China’s growth declining, and with the need to shift from fossil fuels to renewables, the net savings and current account surpluses of the petro-currency countries are likely to erode.

There will be a rising cost of labour and a falling cost of capital. We cannot think of any other time in history when the prices of the two main factors of production will be moving as clearly in opposite directions. Even before demographics start pushing wage growth up, the price of capital goods has already been falling. As wages begin to rise, compensating for more expensive labour will be easier thanks to a lower cost of capital goods. The resulting increase in productivity will somewhat temper the increase in wages and inflation. The savings and investment lens gives us another way to view this response. Given significantly cheaper capital goods, the cost of accumulating a given stock of capital uses up a smaller amount of the economy’s stock of savings. To some extent, this can counter the savings deficit created by ageing demographics and somewhat temper the rise in both the interest rate and wages.

it highly likely that the fiscal position will not move sufficiently strongly into surplus to offset the larger deficits that we expect to see within the private sector. Because fiscal deficits were not sufficient to equilibrate the economy over the last 30 years, central banks were forced to do so by lowering interest rates, ‘the only game in town’.2 In the same way in future, we expect real interest rates to have to rise in order to play the same equilibrating role because the public sector will not save enough.

‘Why Have Interest Rates Fallen Far Below the Return on Capital?’ (2019), has been that aversion to risk and illiquidity has driven an increasing wedge between the return on capital and riskless interest rates. Because the required return on capital remained high, thereby deterring investment, riskless interest rates had to be lower in order to equilibrate the macroeconomy.

What does remain a serious question is why conditions in which profitability has been so high, and both equity prices extremely high and interest rates so low, has not led to a much greater demand for corporate investment.

During these last 30 years, central bankers have remained the best friends of Ministers of Finance. By bringing interest rates steadily downwards, they have enabled the debt burden of sharply rising debt ratios to be completely offset. In future, this is going to change, and in a way that will make life for both those parties more difficult. Rising nominal interest rates, at a time when the prospect is for continuing, and, in some cases, worsening fiscal deficits, and still sharply rising debt ratios, is going to make the life of Ministers of Finance, and Prime Ministers, considerably more problematical. Moreover, the rise in debt ratios in the corporate sector, outside of the banks, is going to mean that the attempt to maintain the inflation target may leave the corporate sector, and the macroeconomy, at greater risk of default and recession.

Although global inequality has started to fall, as inequality between countries has declined quite sharply, inequality within countries has in the vast majority of cases risen, in many cases rather strongly, reversing the decline that took place from about 1914 until about 1980. Thus, the earlier hypothesis, embodied in the Kuznets2 curve, whereby economic development would initially cause inequality to rise, but peak and then fall continuously, appears to have become refuted.

There are two main constituent reasons for such trends in inequality. The first, already discussed in Chapter 3, is that trend growth in the returns to capital have been much stronger than the increase in real wages over this same, three-decade, period. The second main constituent reason for the increase in inequality is that the return to human capital, as proxied by educational attainment, has risen alongside the return to fixed and financial capital. In contrast, the return to muscle-power and simpler repetitive tasks has stagnated.

The implication of all this, i.e. that the very poorest have been protected, whereas the return to human and fixed capital has soared relative to the return to the unskilled and semi-skilled, has been that the lower middle class, say between the 20th and 70th income percentile, has come out the worst. Another aspect of this same phenomenon has been that mid-skilled jobs have fallen relative to both low- and high-skilled jobs, see Diagram 7.4, so that those in the lower middle class who could not raise their human capital were forced back into lower skilled jobs,

why would such technological developments change the slope and/or position of the Phillips curve? With the same level of overall unemployment, why would the associated aggregate wage/price outcome be less? Here the suggestion is that workers in the unskilled (gig) economy may have less relative bargaining power, and are less unionised, than those who previously worked in semi-skilled areas.

Demand management, and full employment policies, greatly strengthened the bargaining power of workers and trade unions. The alternative to not agreeing to the employer’s wage offer would be another job elsewhere, rather than unemployment. Also, as noted in Chapters 3 and 5, demography led to an improving dependency ratio. Union membership generally rose until about 1980

The growing bargaining power (relative to employers) of labour between 1945 and 1980 meant that the underlying NRU was increasing commensurately, perhaps to as much as 5.5%. It is deeply ironic that Keynesian demand management led inexorably to a much higher NRU.

Thus the fact that the slope of the calculated relationship between unemployment (or the output gap) and wage (price) inflation appeared to become more horizontal in these later decades may be just an artefact of better monetary policies and fewer supply shocks, rather than representing any change to the underlying structural relationship.

This finding has several implications. First, as long as there remains a sizeable buffer of elderly still prepared to switch elastically between work and retirement, then the Phillips curve will appear to be more horizontal, since employers can fill job vacancies from that source (as well as from migrants) rather than having to raise wages. Second, the existence of a reserve army of elderly has meant that the NRU will have fallen, since one can run the economy at a higher pressure of demand so long as that reserve army acts as a safety valve.

the CSI index provides a new window on movements in the rate of inflation. Because the CSI index tends to focus its weights on sectors with locally determined prices, it provides a way to separate out prices that are domestically determined from prices that are heavily influenced by international conditions. By using both inflation components and filters that eliminate trends and focus on cyclical variation, a different picture of the stability of the Phillips curve emerges. Whereas the standard accelerationist relationship between changes in inflation and gaps has flattened, the relationship between the weighted cyclical components and cyclical activity is substantially more stable.

not only is the long-run Phillips curve vertical at the NRU (u*), but also the position of u* is continuously and systematically shifting owing to longer-run demographic, political and economic forces.

The wedge between the 1% growth rate of Japan’s total output and the 1% average decline in its workforce is the contribution of productivity. Diagram 9.1 shows Japan’s outperformance over almost every advanced economy when it comes to output per worker.

Japan’s corporates showed a dynamism in overseas investment that was in sharp contrast to the desultory performance at home. O-FDI appears to be a safety valve designed to escape headwinds from local demand and expensive labour in favour of the dual tailwinds of strong overseas growth and cheap labour delivered by global demographic tailwinds. O-FDI was strong even during the ‘lost decade’ and has continued its rich form since.

Corporate Japan aggressively reduced the leverage it had built up at home. The domestic debt/GDP ratio for non-financial companies fell from a peak of 147% of GDP in 1994 to 97% of GDP in 2015, offset by public sector leverage that rose throughout that period. From the point of view of the corporate sector, however, leverage had to be reduced. Basic math says reducing the pace of borrowing or paying back debt when revenues are not growing means that other spending has to be pared back. That is what domestic corporate investment in Japan shows

IMF estimates (IMF 2011, Japan Spillover Report) suggest that labour costs are the prime motivation when looking at the shifting patterns of location and production, while growth in the destination country comes in second. METI’s survey suggests the opposite order as a rationale, by a wide margin. Firms’ responses suggest that 70% think demand in the destination market is the key motivation, while the importance of qualified and inexpensive labour has fallen in recent years.

Investment: the Yen value of outbound FDI increased threefold between 1996 and 2012, while the ratio of investment made in foreign affiliates, as compared to investment at home by domestic firms, increased 10-fold between 1985 and 2013. Number of affiliates: Japanese companies owned around 4000 overseas affiliates in 1987. That rose rather quickly to around 12,600 by 1998 and stands at 25,000 in METI’s 2018 survey. Employment: Overseas affiliates employment stood at 2.3 million in 1996. That number was 5.6 million in 2016.

The overseas capital investment ratio (the ratio of capital investment made in foreign affiliates vs domestic companies) stood at 3% in 1985, quadrupled to 12% by 1997 and reached 30% by 2013. The recent decline in the overseas-domestic investment ratio is one of the rare occasions that domestic investment has risen and overseas investment has fallen. Over the critical period when Japan’s corporate sector was written off, overseas investment has handsomely outperformed domestic investment

Japanese companies with overseas operations produced nearly 40% of their output abroad, while the overseas production ratio (manufacturing sector production by overseas affiliates compared to production within Japan) stood at 25% in 2017 (Diagram 9.6). For the key transportation sector, that ratio stands just below 50%.

‘Only a fraction of the profits generated from overseas operations are repatriated to Japan and a significant portion is reinvested abroad for further expansion of overseas operations’ according to Kang and Piao (2015). Why were profits not repatriated? If the objective of Japanese firms was partly to exploit lower labour costs and an expanding market overseas, then an external expansion would almost require profits generated abroad to be retained there for further expansion. The dramatic increase in capacity and employment abroad seems to suggest this is, indeed, what happened. Moreover, Japan’s ‘dividend exemption policy’ meant that firms were disincentivised from repatriating profits back home

One reason behind the downward pressure on wages in Japan can be traced back to the receding footprint of manufacturing and the expanding role of services, and hence in turn to the importance of the global factors that led to this reallocation. The local dynamics behind that reallocation are relatively straightforward: The aftermath of the asset bust and the two headwinds, tepid growth and an expensive and shrinking pool of labour, led to an investment recession. The manufacturing sector, least able to protect itself at home, begins to raise its productivity in three ways. First, it freezes any further increases in the capital stock and then slowly reduces its labour input. In doing so, the capital available per worker (a basic measure of productivity) slowly starts to rise. Second, manufacturing production slowly starts to get offshored. Third, selecting which activities to offshore completes the process—corporate Japan keeps the design and very high technology parts of the production at home and moves the more mechanical parts of the production line overseas. The manufacturing sector was unwilling to absorb its historical share of workers, and the share of manufacturing employment to total employment fell. The services sector (whose role in the economy was kept steady by the steady nature of consumption) began to face an increasing supply of workers. The share of services in total employment then rose. In order to protect its profitability, the services sector then drove a wedge between prices and wages, but by pushing wage growth lower. This was in part due to the dynamics we have described just above, but also partly due to the changing nature of Japan’s labour market as it sought out ways to escape its institutional customs. Bottom line: activity and jobs were reallocated both outside Japan’s borders and within Japan as the corporate sector strategically and purposefully raised productivity to protect itself. Those efforts need to better recognised not only for delivering Japan’s exit from the lost decade, but also with delivering productivity per worker that outperformed almost every other advanced economy in the world.

In Japan, the loyalty of insider workers is mostly to their company, rather than to a trade union, and the counterpart commitment from the employers is to maintain employment during downturns. So, the Phillips curve in this respect is very flat, with more of the adjustment to cyclical forces being felt in hours worked than in either unemployment or wages. Japan’s local customs of long-term employment make mass layoffs and job destruction unviable options.

The role of part-time, i.e. non-regular, employees grew as cost pressures increased. Their share in total employment rose from about 13% in 1990 to just under 30% by 2018. From the firm’s perspective, a fall in the ratio of insiders to outsiders was important very simply because ‘outsiders’ were not given long-term contracts which made their wages easier to suppress. So strong was the incentive to change this ratio that even in periods when full-time employees were actually being laid off, employment growth for part-time employees remained positive and even rose. In a nutshell, dimming prospects for growth required that firms reduce costs to protect themselves. The customs in the labour market, however, would not allow for a rapid, Western-type adjustment in which layoffs pushed the unemployment rate higher rapidly. Instead, firms employed a far more complex strategy that changed the structure of employment and forced wages and hours to do most of the adjustment.

Japan is not alone in seeing higher participation rates among the pre-retirement cohort. There are at least two reasons for this general trend. First, many have realised that they will live longer so that their planned savings look inadequate. Second, there has been a general degradation of pension benefits (designed to reduce the government’s fiscal burden).

Ageing economies can try to offset demography at home and abroad. At home, ageing economies have three options. First, use technology to offset the negative shock from labour to the production function. Second, raise the participation rate so that people work longer as well. Third, advanced economies can use some labour from abroad, particularly from emerging economies. If importing labour from abroad looks politically unrealistic, then perhaps capital can be exported abroad. There it can be converted into goods and services and repatriated to the advanced economies, the ones exporting the capital. The role of India and Africa—the demographically endowed parts of the world—has been advanced in this regard,

Elderly care in its broadest sense is a labour-intensive process, but it doesn’t necessarily add to future national output growth in a way that other services sector employment does. Put differently, much of patient care is a consumption good rather than a capital good that creates value even in the future.

participation rates of the workforce have already gone up considerably, thanks to greater participation of the 55–64 cohort and especially women. How much further can this rise in the future? In other words, if much of the increase in participation needed has already been realised today, there is less room for improvement for the future when the size of the demographic problem becomes more severe

Net migration flows into the AEs and out of EMEs peaked in 2007 at about 24 million each. When normalised against the size of the population, however, these flows are far too small to make a difference

this more direct route of transferring labour between economies is unavailable, then capital can flow to the labour abundant economies. These flows of capital can be combined with the local supply of labour in the labour-abundant economies to produce goods and services which, in turn, can be exported back to labour-deficient economies.

We think India will beat China in global growth over the next decade, and perhaps even the one after that. However, it will not be able to lift world growth the way China did for three reasons: First, the global environment is materially different in two ways. The decline in nominal and real interest rates during (and caused to no small extent by) China’s ascent created benign conditions at home in the AEs so that the ascent of China was not seen as a zero-sum game.

India has had a rich history of trading over the centuries, aided by empires that stretched over the bulk of its mass, but its disjointed social structure has often been a hindrance in creating a solid economic foundation.

Third and most important, India will be able to attract global capital to its shores, but the lack of administrative capital and its system of democratic checks and balances will not allow a single-minded, China-esque model of growth to materialise.

Inevitably, that means the private sector is to be the vehicle that drives India’s growth. In turn, it is the extent to which the private sector is able and willing to grow that will determine the path of India’s growth. Unlike the growth of SOEs under state patronage, the private sector needs an effective and level playing field to thrive. Thus, a lot depends on how quickly and efficiently India’s administration can reform and deregulate the economy.

Africa’s population in 2019 stands at approximately 1.32 billion, almost identical to India’s 1.37 billion residents, but it hosts that population over an area that is almost ten times the size of India’s 3.2 million square kilometres of territory. Over that area, Africa is made up of 54 countries. A key problem that Africa faces, therefore, is its fragmentation. Fifty-four national policies, each with their own domestic frictions like India’s, will mean a much greater problem when it comes to coordinating policies for growth. It also means that migration within Africa is far more difficult because of national borders than it is within India’s state borders.

emerging economies that cannot transform themselves into advanced economies fail not because of the so-called middle-income trap but because of an administrative trap.

Not only have interest rates already reached the effective lower bound (we discount the likelihood of getting policies to abolish cash ), but also we argue that inflation, and with that nominal interest rates, will most likely rise again. The problem is that the key macroeconomic sectors now carry such elevated debt ratios that any sizeable or sharp increases in interest rates would put large chunks of the private sector into solvency problems and add to the fiscal difficulties of Ministers of Finance.

just as raising leverage in banks increases the risks for other stakeholders, including the public at large, in exactly the same way buy-backs in other corporates is a risk-shifting activity.

Fundamentally, a regime of low and falling interest rates makes default on fixed income obligations less likely even if revenue growth slows down. Financially, the low risk of default makes the purchase of high-yielding securities far more attractive when the ‘search for yield’ dominates investment strategies. A combination of the two naturally led to the rapid growth of the issuance of relatively more risky assets.

Of course, if advantage had been taken of the extraordinarily low interest rates to extend the duration of the public debt, then the forthcoming rise in nominal official short-term interest rates would have less effect. But the felt need for an ever more expansionary monetary policy has led instead to a significant reduction in effective durations. In particular, a combination of QE and a ‘floor system’ of paying interest on commercial bank deposits at the Central Bank has meant that the equivalent volume of QE, backed by such deposits, has an effective zero duration. Thus, public sector fiscal finances will feel the full pain of any increase in official interest rates almost immediately. With corporate sector finances having become increasingly fragile, and (populist) political calls for keeping interest rates low, the Central Bank will become under intensifying pressure to keep any increases in interest rates gradual and limited. But if such interest rate increases remain gradual and small, then the present incentives to extend and expand debt finance remain in place. That is the debt trap in which so many of our countries have now become ensnared.

Real interest rates have become exceptionally low, partly because demographic pressures, particularly in China, have led to savings ‘gluts’, while investment ratios outside of China, as earlier argued, have remained extremely low, partly under the influence of the globalised availability of additional cheap labour. Both these factors are going into reverse. As the dependency ratio rises, personal sector savings ratios are likely to decline, unless governments consciously restrict the future generosity of their pensions and medical assistance for the aged, which could be politically challenging. At the same time the recovery in the power of labour, as workers become scarce, and taxation rises to meet extra public sector expenditures, will lead to rising real unit labour costs. In order to offset that, corporates are likely to increase their investment demand. So, the likelihood is that the balance between investment and saving, i.e. the demand and supply of loanable funds, may well lead to a recovery in real interest rates. If so, forthcoming pressures may lower growth rates, at the same time as real interest rates rise, making it increasingly difficult simply to grow out of current high debt ratios.

Finally, during periods of stress around debt, an income stream of fixed payments that is supposed to protect creditors turns out to be a dubious asset at best. History is littered with episodes of default on debt that have gone hand-in-hand with macroeconomic upheaval. Thus, even the benefits to creditors from holding debt versus equity are not clear and obvious.

mainstream approach to inflation has been based on monetarism and Keynesian demand management. To a monetarist, or to a gold-enthusiast, the answer to explaining such trends is straightforward. Advanced economies moved from a gold standard regime to a fiat money regime. Fiscal dominance allowed politicians to bribe the electorate with their own money and inflation ensued. Then stagflation took over in the horrible 1970s, and conditions got so bad that a move to Central Bank Independence (CBI) restored some vestiges of monetary constraint on politicians.

A consequence of the strengthening of labour’s bargaining power was that the Natural Rate of Unemployment was rising in the background, an ironic side effect of the deployment of Keynesian demand management to raise the average level of employment.

Inflation, it is regularly repeated, is a monetary phenomenon, and Central Banks can create money. How, then, can we have a persistent problem of lower than targeted inflation? Of course, we are told that this problem is due to the zero, or effective, lower bound to nominal interest rates. But when inflation remains around 1%, as now in 2019, the ELB only becomes a serious constraint when the equilibrium real interest rate, r*, itself becomes negative, falling far below its prior typical values of 2.5 to 3.5%. And that is a real, and not just a nominal, monetary problem. The mainstream explanation of our times is precisely that, an r* that has indeed become negative. The secular stagnation thesis involves a variety of arguments (inequality and even the need for ageing societies to invest less are in the mix), to explain why r* has fallen and will remain low for the foreseeable future. Empirical estimates of r* regularly show estimates that are negative or very close to zero, backing up the claims of the secular stagnationists.

Our approach differs from the mainstream in this field on at least three important issues. First, we are not as sanguine about the future of personal savings as the mainstream. The consumption assumptions of their models simply do not match the consumption dynamics that an ageing society will display. Second, we are more optimistic on corporate investment when faced with a declining workforce. We agree with Andrew Smithers that a serious governance problem in capitalist economies has hindered investment, particularly in the USA. Finally, the mainstream view sees debt and demography singing from the same hymn sheet in driving growth, inflation and interest rates lower for the foreseeable future. We see the two in conflict, with debt a gigantic block that the irresistible forces of demography will eventually move out of its way. In turn, that will put monetary and fiscal policymakers in conflict with each other.

Part of the answer is that investment, like production, has been off-shored to emerging Asia, see especially Chapter 9 on Japan. If so, the curtailment of globalisation will bring some boost to domestic investment. Another part of the answer could be that the weakness of labour bargaining power has allowed employers in the non-traded services sector to raise profits by lowering wages in the gig economy, rather than going through the more difficult process of raising employee productivity, notably via investment

Inflation will rise considerably above the level of nominal interest rates that our political masters can tolerate. The excessive debt, amongst non-financial corporates and governments will get inflated away. The negative real interest rates that may well be necessary to equilibrate the system, as real growth slows in the face of a reversal of globalisation and falling working populations, will happen. Even if central banks feel uncomfortable with such higher inflation, they will be aware that the continuing high levels of debt make our economies still very fragile. And if they try to raise interest rates in such a context, they will face political ire to a point that might threaten their ‘independence’.

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