Progress Daily

People of the mid-19th Century, like those before them, expected to develop chronic diseases by their 40s or 50s.

In 1900, 13% of people who were 65 could expect to see 85. Now, nearly half of 65-year-olds can expect to live that long.

Scientists used to say that the reason people are living so long these days is that medicine is keeping them alive, though debilitated. But studies like one Dr. Robert W. Fogel directs, using records of of Union Army veterans, have led many to rethink that notion.

The study involves a random sample of about 50,000 Union Army veterans. Dr. Fogel compared those men, the first generation to reach age 65 in the 20th century, with people born more recently.

He discovered that almost everyone of the Civil War generation was plagued by life-sapping illnesses, suffering for decades. And these were not some unusual subset of American men — 65% of the male population ages 18 to 25 signed up to serve in the Union Army.

80% had heart disease by the time they were 60, compared with less than 50% today. By ages 65 to 74, 55% of the Union Army veterans had back problems. The comparable figure today is 35%.

Men living in the Civil War era had an average height of 5-foot-7 and weighed an average of 147 pounds. Today, men average 5-foot-9½ and weigh an average of 191 pounds.

Common chronic diseases — respiratory problems, valvular heart disease, arteriosclerosis, and joint and back problems — have been declining by about 0.7 percent a year since the turn of the 20th century. And when they do occur, they emerge at older ages and are less severe.

The reasons, Dora Costa and others are finding, seem to have a lot to do with conditions early in life. Poor nutrition in early years is associated with short stature and lifelong ill health, and until recently, food was expensive in the United States and Europe.

Dr. Fogel and Dr. Costa looked at data on height and body mass index among Union Army veterans who were 65 and older in 1910 and veterans of World War II who were that age in the 1980s. Their data relating size to health led them to a prediction: the World War II veterans should have had 35% less chronic disease than the Union Army veterans. That, they said, is exactly what happened.

They also found that diseases early in life left people predisposed to chronic illnesses when they grew older.

Men who had respiratory infections or measles tended to develop chronic lung disease decades later. Malaria often led to arthritis. Men who survived rheumatic fever later developed diseased heart valves.

And stressful occupations added to the burden on the body.

People would work until they died or were so disabled that they could not continue, Dr. Fogel said. “In 1890, nearly everyone died on the job, and if they lived long enough not to die on the job, the average age of retirement was 85.” Now the average age is 62.

A century ago, most people were farmers, laborers or artisans who were exposed constantly to dust and fumes.

In one study, Dr. David JP Barker examined health records of 8,760 people born in Helsinki from 1933 to 1944. Those whose birth weight was below about six and a half pounds and who were thin for the first two years of life, with a body mass index of 17 or less, had more heart disease as adults.

Another study, of 15,000 Swedish men and women born from 1915 to 1929, found the same thing. So did a study of babies born to women who were pregnant during the Dutch famine, known as the Hunger Winter, in World War II.

That famine lasted from November 1944 until May 1945. Women were eating as little as 400 to 800 calories a day, and a sixth of their babies died before birth or shortly afterward. But those who survived seemed fine. Even their birth weights were normal.

But now those babies are reaching late middle age, and they are starting to get chronic diseases at a much higher rate than normal. Their heart disease rate is almost triple that of people born before or after the famine. They have more diabetes. They have more kidney disease.

The middle-aged people born during the famine also say they just do not feel well. Twice as many rated their health as poor, 10% compared with 5% of those born before or after the famine.

The flu pandemic arrived in the United States in October 1918 and was gone by January 1919, afflicting a third of the pregnant women in the United States. Dr. Douglas V. Almond compared two populations: those whose mothers were pregnant during the flu epidemic and those whose mothers were pregnant shortly before or shortly after the epidemic.

Dr. Almond found that the children of women who were pregnant during the influenza epidemic had more illness, especially diabetes, for which the incidence was 20% higher by age 61. They also got less education — they were 15% less likely to graduate from high school. The men’s incomes were 5% to 7% lower, and the families were more likely to receive public assistance.

“So Big and Healthy Grandpa Wouldn’t Even Know You,” by Gina Kolata

During the forty year period 1950-1990 world population grew at an annual rate of just under 2%, and total production of goods and services grew at 4% per year. This means that production per person grew at more than 2%, implying that income per person more than doubled over these years. These figures refer to the entire world, rich and poor alike. Nothing remotely like this has ever been seen before.

For comparison, during the 18th century world population and production both grew at about 0.33% per year, and average living standards grew not at all. From 1800 to 1950, when the industrial revolution began to transform the lives of large numbers of people, population grew at 0.7% and production at 1.4%, implying per capita income growth of 0.7%. (All of these figures are taken from Tables 5.1 and 5.2 of Lectures on Economic Growth.)

The figure below plots the course of per capita incomes in five parts of the world since 1750. Groups I, III, and IV are countries largely populated and ruled by Europeans, wherever located, ordered from most to least successful economically. Group II is Japan.

The final curve includes all of Africa and Asia (except for Japan): today, more than two-thirds of the world’s population. British India and Africa are here, along with the subjects of French, Dutch, German, Portuguese, Spanish, and American imperialism. So is China, with its ambiguous role in the colonial age, and those few others that somehow remained outside the empires of Europe and Japan. The striking fact is that these colonial subjects had the same living standards at the end of the colonial period as they had had two centuries earlier.

The British Empire shows up in this figure in two places. British-ruled and largely British-occupied Canada, Australia and New Zealand are included in the top curve, along with the US and the UK. The British-ruled and largely non-British occupied colonies of Africa and Asia are included in the bottom curve.

Showing the British colonies in Africa and Asia separately would have added no information: The pre-1950 histories of the economies in these parts of the world all show living standards that are roughly constant at perhaps $100 or $200 above subsistence levels. There are no differences in this regard between colonies and independent nations in this group (Japan, of course, excepted) or between the subjects of any one of the European empires or another. There is no reason to think that British or other imperialism caused the economic stagnation shown on the figure: Stagnation at income levels slightly above subsistence is the state of traditional agricultural societies anywhere and any time. But neither did the modern imperialisms  alter or improve incomes for more than small elites and some European settlers and administrators.

The income curve for Africa and Asia turns up a bit after 1950. This may not look like much in the picture but it amounted to more than a tripling of incomes. The fact that living standards for masses of people in these populous, poor societies finally began to grow after independence is what made possible the high world-wide growth rates quoted at the beginning of these comments. The main economic event of the late 20th century was this diffusion of the industrial revolution to non-European societies, begun in Japan half a century earlier. A central question is why it did not begin much earlier, during the colonial period, at the same time that the industrial revolution was spreading throughout Europe.

That it did not do so is especially surprising and puzzling in view of fact that there were large-scale increases in the volume of world trade in the 19th century and considerable investment by the British in the Americas, India, and elsewhere. The security of foreign investment is an advantage of imperialism: British military power made Indian railroad bonds as safe for British savers as home investments were. India was surely better off with the British-built railroads than without them, but investments like these did not lead to anything like the kind of economic development we have seen in the post-colonial period.

The second, post-colonial phase of globalization has been associated with unprecedented growth in the living standards of hundreds of millions of people while the first, colonial phase was not.

“Colonialism and Growth,” by Robert E. Lucas, Jr.

Already much of our software and data is moving to giant remote servers connected to the Internet. Our photos, music, software applications like Microsoft Word, and just about everything else we use a computer for will soon be accessible to us wherever we go.

The cellphone is becoming more like a PC. The next great era of computing will be about smaller, cheaper, more-powerful portable devices.

A major innovation we’re seeing right now is vastly-improved voice-recognition software. While it only works on the fast processors of a PC today, the inexorable growth of computing power will soon take that kind of power into your cellphone. So long keyboard!

In the next phase, the devices essentially disappear, due to quantum computing.

“Coming soon: Google on your brain,” by David Kirkpatrick

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Matthew Nagle, a 26-year-old quadriplegic, was hooked up to a computer via an implant smaller than an aspirin that sits on top of his brain and reads electrical patterns. Using that technology, he learned how to move a cursor around a screen, play simple games, control a robotic arm, and even turn his brain into a TV remote control. All while chatting amiably with the researchers. He even learned how to perform these tasks in less time than the average PC owner spends installing Microsoft Windows.

Nagle was able to accomplish all this because the brain has been greatly demystified in laboratories over the last decade or so. Researchers unlocked the brain patterns for thoughts that represent letters of the alphabet as early as 1999.

Neurodevices - medical devices that compensate for damage to the brain, nerves, and spinal column - are a $3.4 billion business that grew 21 percent last year, according to NeuroInsights. There are currently some 300 companies working in the field.

Already this kind of technology can enable a hooked-up human to write at 15 words a minute - half as fast as the average person writes by hand. Remember, though, that silicon-based technology typically doubles in capacity every two years.

So if improved hardware is all it takes to speed up the device, Cyberkinetics’ chip could be able to process thoughts as fast as speech - 110 to 170 words per minute - by 2012.

Last year, Sony took out a patent on a game system that beams data directly into the mind without implants. It uses a pulsed ultrasonic signal that induces sensory experiences such as smells, sounds and images.

And Niels Birbaumer has developed a device that enables disabled people to communicate by reading their brain waves through the skin, also without implants.

“Surfing the Web with nothing but brainwaves,” by Chris Taylor

The human family originated in one small corner of East Africa a few million years ago – wandered, separated, diversified, and became strangers to one another.

The reversal of this trend has occurred only fairly recently and only because of advances in technology. The domestication of the horse permitted us to send messages (and ourselves) hundreds of miles in a few days. By the 18th century, advances in sailing ship technology allowed people to travel from Europe to China in about 2 years. The real binding up and deprovincialization of the planet requires technology that allows much faster communication than the horse or ship, that’s available worldwide, and that’s cheap enough to be used by the average person.

Today we communicate at the speed of light. From the speed of horse or ship to the speed of light is an improvement by a factor of almost 100,000,000. Of course our societies have not yet caught up.

Billions and Billions, by Carl Sagan

When quantitative geneticists estimate the heritability of IQ, they are generally relying on studies of twins. Identical twins are in effect clones who share all their genes; fraternal twins are siblings born together — just half of their genes are identical. If heredity explains most of the difference in intelligence, the logic goes, the IQs of identical twins will be far more similar than the IQs of fraternal twins. And this is what the research has typically shown. Only when children have spent their earliest years in the most wretched of circumstances, has it been thought that the environment makes a notable difference.

In combing through the research, though, Eric Turkheimer noticed that the twins being studied had middle-class backgrounds. (Poor people seldom volunteer for research projects.)

Turkheimer searched for data on twins from a wider range of families. He found a sample from the 1970s of more than 50,000 American infants, many from poor families, who had taken IQ tests at age 7. He found that, as anticipated, virtually all the variation in IQ scores for twins in the sample with wealthy parents can be attributed to genetics. Among the poorest families, though, the IQs of identical twins vary just as much as the IQs of fraternal twins. The impact of growing up impoverished overwhelms these children’s genetic capacities.

This finding was confirmed in a study published last year. An analysis of the reading ability of middle-aged twins showed that even half a century after childhood, family background still has a big effect — but only for children who grew up poor. Meanwhile, Turkheimer is studying twins who took the National Merit Scholarship exam, and has found that, though these students mostly come from well-off homes, variations in family circumstances still matter.

Consistent with the proposition that intelligence is mainly inherited, studies have almost always found that adopted youngsters more closely resemble their biological than their adoptive parents. But researchers in France noted a shortcoming in these adoption studies. Since poor families rarely adopt, those investigations have had to focus only on youngsters placed in well-to-do homes. What’s more, because most adopted children come from poor homes, almost nothing is known about adopted youngsters whose biological parents are well-off.

Christiane Capron and Michel Duyme combed through thousands of records from French public and private adoption agencies. Regardless of whether the adopting families were rich or poor, children whose biological parents were well-off had IQ scores averaging 16 points higher than those from working-class parents. The average IQ of children from well-to-do parents who were placed with families from the same social stratum was 119.6. But when such infants were adopted by poor families, their average IQ was 107.5 — 12 points lower. Youngsters adopted by parents of similarly modest means had average IQs of 92.4, while the IQs of those placed with well-off parents averaged 103.6.

One study analyzed French youngsters adopted between the ages of 4 and 6. Most had been abused or neglected as infants, then shunted from one foster home or institution to the next, and they scored an average of 77 on IQ tests. Nine years later, when they retook the tests, all of them did better. The amount they improved was directly related to the adopting family’s status. Children adopted by farmers and laborers had average IQ scores of 85.5; those placed with middle-class families had average scores of 92. The average IQ scores of youngsters placed in well-to-do homes climbed more than 20 points, to 98 — a jump from borderline retardation to a whisker below average.

In Meaningful Differences in the Everyday Experience of Young American Children, Betty Hart and Todd Risley find that by the time they are 4 years old, children growing up in poor families have typically heard a total of 32 million fewer spoken words than those whose parents are professionals. That language gap translates directly into stunted academic trajectories.

“After the Bell Curve” by David L. Kirp

In virtually every human society, both civil and religious law provide long lists of behaviors that are illegal or immoral — unless they are responses in kind.

Research shows, though, that while people think of their own actions as the consequences of what came before, they think of other people’s actions as the causes of what came later.

In a study conducted by William Swann pairs of volunteers played the roles of world leaders who were trying to decide whether to initiate a nuclear strike. The first volunteer was asked to make an opening statement, the second volunteer was asked to respond, the first volunteer was asked to respond to the second, and so on. At the end of the conversation, the volunteers were shown several of the statements that had been made and were asked to recall what had been said just before and just after each of them.

When volunteers were shown one of their own statements, they remembered what had led them to say it. But when they were shown one of their conversation partner’s statements, they remembered how they had responded to it.

In a study conducted by Sukhwinder Shergill, pairs of volunteers were hooked up to a mechanical device that allowed each of them to exert pressure on the other volunteer’s fingers.

The researcher began the game by exerting a fixed amount of pressure on the first volunteer’s finger. The first volunteer was then asked to exert precisely the same amount of pressure on the second volunteer’s finger. The second volunteer was then asked to exert the same amount of pressure on the first volunteer’s finger. And so on.

The two volunteers took turns applying equal amounts of pressure to each other’s fingers while the researchers measured the actual amount of pressure they applied.

Volunteers typically responded with about 40 percent more force than they had just experienced. What began as a game of soft touches quickly became a game of moderate pokes and then hard prods.

Each volunteer was convinced that he was responding with equal force and that for some reason the other volunteer was escalating.

Neither realized that the escalation was the natural byproduct of a neurological quirk that causes the pain we receive to seem more painful than the pain we produce, so we usually give more pain than we have received.

“He who cast the first stone probably didn’t,” by Daniel Gilbert

If you want to remember something, study it right before you go to sleep.

Sleep… plays an active role in consolidating memories. “Rather than being a passive state, it’s a dynamic neurobiological process,” said Dr. Jeffrey M. Ellenbogen, the lead researcher. “It turns out that the process of memory doesn’t end when we stop studying, but continues during sleep. That’s important to all of us.”

If you want to avoid the memory decline associated with old age, stop believing in that association.

“The implication is that some of the things we say about ourselves in conversation — joking about ’senior moments’ is a perfect example — these kinds of comments may in fact undermine our own memory at the time we’re saying them,” Dr. Mary Lee Hummert said. “And the fear is that it has a cumulative effect, that it becomes a negative feedback cycle.”

“Two memory tips,” Ann Althouse

Dictatorships and democracies do not differ on the average in their annual rates of growth of total income. Between 1951 and 1999, total GDP grew at the annual rate of 4.4% under dictatorships and 3.69% under democracies. Democracies are much more common in developed countries, where incomes tend to grow more slowly. If we assume that dictatorships exist under the same conditions as democracies, we will conclude that the average rate of growth of total income would have been about 4.24% under dictatorships and 4.06% under democracies — a negligible difference.

Dictatorships and democracies do not differ on the average in their annual rates of growth of total income. Between 1951 and 1999, total GDP grew at the annual rate of 4.4% under dictatorships and 3.69% under democracies. Democracies are much more common in developed countries, where incomes tend to grow more slowly. If we assume that dictatorships exist under the same conditions as democracies, we will conclude that the average rate of growth of total income would have been about 4.24% under dictatorships and 4.06% under democracies — a negligible difference.Mortality rates are higher and life expectancies lower under authoritarian regimes. Fertility and population growth rates are higher under dictatorships. Very poor countries have fertility rates of about six births per woman regardless of their regime, while very wealthy countries converge to replacement fertility rates under both regimes. But within the entire intermediate range, from per capita income of US$1,000 to $12,000 (in 1985 purchasing parity terms), fertility is higher under dictatorships. When regimes are matched for social and economic conditions, statistics reveal that an average woman has one-half of a child more under dictatorship than under democracy.

Dictatorships and democracies do not differ on the average in their annual rates of growth of total income. Between 1951 and 1999, total GDP grew at the annual rate of 4.4% under dictatorships and 3.69% under democracies. Democracies are much more common in developed countries, where incomes tend to grow more slowly. If we assume that dictatorships exist under the same conditions as democracies, we will conclude that the average rate of growth of total income would have been about 4.24% under dictatorships and 4.06% under democracies — a negligible difference.Mortality rates are higher and life expectancies lower under authoritarian regimes. Fertility and population growth rates are higher under dictatorships. Very poor countries have fertility rates of about six births per woman regardless of their regime, while very wealthy countries converge to replacement fertility rates under both regimes. But within the entire intermediate range, from per capita income of US$1,000 to $12,000 (in 1985 purchasing parity terms), fertility is higher under dictatorships. When regimes are matched for social and economic conditions, statistics reveal that an average woman has one-half of a child more under dictatorship than under democracy.The consequence is that per capita, as distinct from total, income grows faster under democracies. The rate of annual growth of per capita income was 1.84% under dictatorships and 2.26% under democracies. Correcting for exogenous conditions, we still conclude that per capita incomes grew at the annual rate of 1.93% under dictatorships and at the rate of 2.11% under democracies.

Of the 100 countries with per capita incomes of less than US$2,000 in 1950 or when they became independent, 56 remained equally poor or even poorer decades later. Some countries developed spectacularly, at least quintupling their per capita incomes. Of this list, Japan, Ireland, and Malta were parliamentary democracies during the entire period. Greece was a parliamentary democracy before and after a period of military rule. Botswana had more than one party with reasonably free elections in which the same party always won an overwhelming majority. Singapore and Malaysia had authoritarian regimes during the entire period. Portugal, Spain, South Korea, and Taiwan proceeded from various forms of dictatorship to different forms of democracy. Finally, Thailand has been so politically unstable that its history cannot be summarized. There is nothing to indicate that it takes one regime or the other to generate spectacular successes.

The list of economic disasters is much longer. 19 countries that were independent before 1990 had lower incomes at the end than at the beginning of the period. Among them, Kiribati and Papua New Guinea were parliamentary democracies throughout. Five countries remained under different dictatorships during the entire period. Seven countries started as authoritarian and ended as democracies, but the transition typically occurred too late to impact observed growth patterns. Somalia began as a democracy and disintegrated under military rule. Finally, Suriname had a convoluted political history. Almost all these countries were ruled by dictatorship during most of their histories, having often experienced periods of civil strife and a lack of political institutions.

Economic growth under dictatorships is highly sensitive to any visible signs of political mobilization and to turnover of heads of government, while under democracy the same phenomena are an expected part of life and have no effect on growth rates. Dictatorships exhibit much higher variance in economic performance.

Democratic regimes are more frequent in the more developed countries, while dictatorships predominate in poor ones.

Taiwan developed under an authoritarian regime, reached an annual per capita income of US$10,610 in 1996, and transitioned to democracy. But this is not evidence of the role of development. By 1996, the Taiwanese regime was 47 years old. If this regime had a 0.0226 chance of collapsing in any random year — the average probability for all dictatorships — it would have had only a 35% chance of surviving past 46 years even if it had remained as poor as it was in 1949. Most likely, the Taiwanese regime decided to hold elections because it needed to mobilize the support of democratic countries in its geopolitical conflict with China, a reason unrelated to development.

Suppose that all dictatorships face the same chance of falling during a particular year for purely idiosyncratic reasons. Some dictatorships succumb to these events, but most escape them. Some of those that manage to survive grow economically. The observed result will be that dictatorships die in countries with high incomes.

Some of the factors that drive the transitions to democracy are related to income. First, countries with intermediate income levels are more likely to have experienced regime transitions in the past, and dictatorships last for shorter periods in countries that have previously experienced democracy. Secondly, dictatorships that emerge at intermediate income levels tend to be military dictatorships, which have shorter lives than civilian ones.

The per capita income thresholds at which democracies emerge vary enormously: India had a per capita income of about US$556 in 1947, the United States had an income of roughly US$1,100 in 1830 — years when both countries established lasting democracies. Meanwhile East Germany, Taiwan, and Singapore remained under the grip of dictatorships even when their incomes surpassed US$10,000.

There is, then, no evidence that democracies are more likely to emerge when a country becomes modernized. Rather, the evidence is overwhelming that if democracy emerges in a country that is already modern, then it is much more likely to survive. No democracy ever fell in a country with a per capita income higher than that of Argentina in 1975 — US$6055. While, throughout history, about 70 democracies have collapsed in poorer countries. In contrast, 35 democracies spent a total of 1,000 years under more affluent conditions, and not one collapsed.

The probability that democracy survives increases monotonically with per capita income. Between 1951 and 1999, the probability that a democracy would fall during any particular year in countries with per capita income under US$1,000 was 0.089, implying that their expected life was about 11 years. With incomes in the range of US$1001 to US$3000, this probability was 0.037, for an expected duration of about 27 years. Between US$3001 and US$6055, the probability was 0.013, which translates into about 78 years of expected life. And above US$6055, no democracies have fallen.

If a dictatorship happens to fall in a country with a low per capita income, democracy is not likely to be long-lived. But if it falls in a more affluent country, democracy endures. Development does not generate democracy; rather democracies accumulate in the more developed countries.

“A Flawed Blueprint,” by Adam Przeworski

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Studies suggest more than 50% of doctors would lie to insurance companies to obtain treatment for a patient. A consultant to some of America’s largest public corporations says his polls reveal 20-30% of middle managers have presented fraudulent internal reports.

How can you tell when you’re being deceived?

Dr. Paul Ekman cautions against attributing too much meaning to shifty eyes or squirming for two reasons.

First, even though this kind of nonverbal communication reliably signals emotion, that emotion could just as well be fear of being disbelieved as fear of being caught lying.

Second, pathological liars excel at making their speech and body language sincere.

Ekman: “The most common vocal deception clues are pauses and speech errors. These occur either because the liar may not have worked out his or her lie ahead of time, or because even if they did expect to lie, they did not anticipate your particular question.”

Ekman’s tips:

Know their baseline behavior. Many years of research have proven that it’s incredibly difficult to know if people are lying unless you have prior exposure to their baseline behavior and know how they normally act.

Avoid entering into agreements over the phone. Studies show people are most likely to lie via telephone.

Establish rapport.

Ask for minute details. Liars hate to give detail and often are evasive.

Watch for “false” facial expressions. Even the most practiced liars are unable to produce the minute movements in the upper part of the face that naturally come when certain emotions are felt. For example, if someone truly feels fear or sadness their forehead will crease. And when people are genuinely happy, their eye muscles will be involved in their smile. A sign that someone is feigning an emotion is that the facial expressions or onset or offset of the emotion is too abrupt.

Give them an out. Make it easy for them to tell you the truth. Leave a way out so they can recant their words and tell you the truth.

“Tell-tale signs of a tall tale,” CNN.com

Leon Walras, a 19th-century French economist, was adamant that one could not explain anything in an economy until one had explained everything. Each market — for goods, labor and capital — was connected to every other. Faster car sales in Texas result in an increase in grocery shopping in Detroit, the home of America’s “big three” carmakers. Steep prices for oil lead to lower American interest rates, because the money the Saudis and the Russians make from crude is spent on American Treasury bonds.

Politicians slap tariffs on steel imports to save jobs in Pittsburgh, only to find this costs more jobs in the domestic industries that use the metal. Or they keep zombie companies alive — rolling over their loans, and preserving their employees on the payroll — only to discover they have starved new firms of manpower and credit.

In 1941 Wassily Leontief published The Structure of American Economy, which included a table showing the flow of commodities and services back and forth among America’s households, trading partners and 41 national industries. In Leontief’s blueprint, each industry is represented by an equation. The inputs to the industry are entered on one side of the equation, the industry’s output appears on the other. Since the output of one industry (steel, for example) serves as an input for another (construction), one cannot solve any equation without solving them all simultaneously.

In 1958, William Phillips showed that for long stretches of British history, high unemployment coincided with low wage inflation, and vice versa. Many macroeconomic models therefore featured a trade-off between the two.

But in the 1970s these trusted relationships broke down. And in 1976 Robert Lucas explained why. Unanticipated inflation would erode the real value of wages, making workers cheaper to hire. But if central bankers tried to engineer such a result, by systematically loosening monetary policy, then forward-looking workers would pre-empt them, raising their wage claims in anticipation of higher inflation to come. Cheap money would result in higher prices, leaving unemployment unchanged.

One could not judge how the macroeconomy would respond to a new policy based on its behavior under the old regime.

Timothy Kehoe, in a paper published last year, argued that the models “drastically underestimated” NAFTA’s impact on trade flows (if not on jobs). The modellers assumed the trade pact would allow people to buy more of the goods for which they had already shown some appetite. In fact, the agreement set off an explosion in the exports of many products Mexico had scarcely traded before. Cars, for example, amounted to less than 1% of Mexico’s exports to Canada before the agreement. By 1999, however, they accounted for more than 15%.

“Big questions and big numbers,” The Economist