CONSIDER THREE COUNTRIES, each economically mediocre:
In all three, the average person’s income is about one-fifth the average American’s, which puts them right in the middle of the planet’s income distribution. And these low incomes really matter: about 20 million Indonesians lack even basic toilets, only a quarter of rural Paraguayans have running water at home, and the average Egyptian uses only about one-eighth the electricity of the average American.
So, if you wanted to come up with an immigration policy to make the average person in these three countries better off, what would it be? And better off not just in the short run but in the long run, for generations?
How about this: Let in a lot of people from China every year—maybe 2 percent of each nation’s population—for about a dozen years. That would mean letting in around
2 million a year for Egypt,
150,000 a year for Paraguay, and
5 million a year for Indonesia.
Let in anyone from China who passes a basic background check—no criminal record, high school graduate, maybe a little college or some trade school experience—and encourage them to settle down permanently.
Why? Just look around. For centuries, in every country that’s had a lot of immigration from China, things go pretty well in the Chinese immigrant community. Lots of emphasis on education and entrepreneurship, high savings rates—not a lot to complain about and lots to like. And when you look at countries where the descendants of Chinese immigrants are the majority of the population—think about Singapore and Taiwan—those nations are models of good competent government, low corruption, and excellent success in the battle against COVID-19. They’re comparatively rich, too: the average person in Singapore, for example, is far richer than the average American.
Chinese immigration has a great track record, and here’s the thing: China itself is today just poor enough, and probably will be poor enough for another couple of decades, that tens of millions of Chinese citizens would welcome the chance to settle down overseas if they had a promising opportunity. So, try this policy: Let almost anyone from China move to any of those three countries, and if they stay for five years, then their next five years are tax-free. That’d spur a lot of people to settle down.
Give it enough time, encourage those Chinese immigrants to raise families there, let them pass on their culture to their children. Then let those children try to run for political office or take on top jobs in the business world. That second generation, as well as the third and fourth, will tend to shape the nation’s culture, tend to improve the nation’s government, and tend to import the historic patterns of Chinese success that help make a nation’s industries globally great rather than globally so-so. Yes, there will be social conflict, there will be ethnic backlash, and there may be racial violence—but while there are no guarantees in government policy, a policy to let in people from China has a strong track record, one of the best.
And while a pro-Chinese immigration policy won’t make these three countries as rich as Singapore in fifty years, still, such a policy would be a big improvement over the status quo.
Why? Because immigration, to a large degree, creates a culture transplant, making the places that migrants go a lot like the places they left. And for good and for ill, those culture transplants shape a nation’s future prosperity.
ONE SIGN ECONOMICS IS A SCIENCE is that it teaches you things that you don’t want to believe. And this book tells a true story that this economist sincerely, truly does not want to believe.
It begins in the 1990s, when desktop computers grew powerful. In the field of economics, this new supply of computer power created new demands: demand for easy-to-use number crunching software and demand for data on the global economy. Soon enough those demands were met, and economists were combining computers, software, and datasets to supply a torrent of fact-driven research. One big result: a lot more “inquiry into the nature and causes of the wealth of nations.” That’s the title of Adam Smith’s 1776 book that invented my field.1 But even though you might think economists would naturally put most of their effort into explaining why some nations were so much richer than others, for a long time that question had been a side issue, a scholarly backwater. There were some influential explanations lying around—maybe it’s national savings rates! maybe it’s how market-friendly, how laissez-faire your government is!—but without good statistics, it was hard to sort out great explanations from also-rans.
But in the 1990s, we finally got the data. So, economists spent the early 1990s running endless statistical tests and publishing hundreds of papers to find out exactly what could predict prosperity and what couldn’t. First the easy tests: Was a higher savings rate good news? Yes. Was more capitalism good news? Yes. And so on. Some winners (avoiding war), some losers (inflation doesn’t seem to matter much either way—high inflation is more a symptom of a bad economy than a problem itself).
The search for the root causes of prosperity continued, and so the search for statistical horse races continued. Every time an economist dug up a new dataset with information from dozens of countries on, say, the fraction of the population that was Protestant or how many coups had occurred between 1960 and 1990 or how far the country was from the equator, it was time to publish a new paper. The research kept on coming—and quickly economists moved beyond looking at conventional economic predictors like
degree of free international trade,
exchange rate manipulation, and
level of education
to looking at geographic predictors of prosperity like
total size of the country,
fraction of the country near a coastline, and
prevalence of malaria
and ultimately to historical and cultural predictors like
fraction of the population that was Jewish, Muslim, or Eastern Orthodox,
fraction of the population speaking English, and
whether the country had been a Spanish, British, or French colony.
A lot of papers found a lot of predictors of long-run prosperity, and while the papers didn’t often contradict each other, still economists wondered whether maybe, intentionally or unintentionally, these papers were reporting only the results that made each author’s favorite theories look good. For instance, maybe some economist out there ran dozens of statistical horse races between “fraction Protestant” and other predictors of prosperity, and maybe that economist reported only the results that made the Protestants look good.
I’m not going to say people were hiding the ball—but maybe they were hiding the ball. It’s called the file-drawer problem: if you run a statistical analysis and either you don’t like the results or you’re pretty sure no editor will like the results, you just stick that project in a file drawer and move on to something else.
By the mid-1990s there was a lot of doubt about whether to believe what you were seeing in the journals. The typical paper might report ten to twenty statistical results—regressions, as they’re usually known—and call it a day. That’s better than just reporting one result, but it’s nowhere close to comprehensive. If you really wanted to thoroughly kick the tires, you’d need to run every single potential explanation of national prosperity in a horse race against every single other potential explanation. If you had a list of one hundred potential explanations and wanted to run them all against each other, head-to-head, two at a time, that alone would be (100 × 99)/2 = 4,950 statistical tests. Nobody’s outlandish enough to try that, are they?
Fortunately, Columbia University’s Xavier Sala-i-Martin was. He wanted to run the ultimate horse race between every possible predictor of a nation’s long-run economic performance. He found data from dozens of countries on sixty-two different factors that might predict national prosperity; and then he ran 2 million regressions two different ways, which gave him the title of his 1997 paper, which has been mentioned in over three thousand scholarly papers and books: “I Just Ran Four Million Regressions.”2
My graduate students always chuckle when I tell them the title—they’ve usually taken a statistics class or two, and they know how cumbersome it can be to correctly run one such analysis. And this professor ran four million of them! Now that’s thorough.
So, who won? Skipping some details, here are the top three:
1. Rate of investment in equipment (physical capital: the stuff that fills factories and offices)
2. Number of years the economy has been open to trade (loosely, a measure of freer trade, low taxes on imports)
3. Fraction of the population that is from a Confucian background (predominantly cultures in East and Southeast Asia)
So, in order: one “normal economics” factor, one “political system” factor, and one “cultural” factor. All were positive factors, so more was better.
That paper’s results, combined with other papers along the same lines published in the late 1990s, led to a revolution in how economists saw the wealth of nations. Before then, economists had been spending maybe 80 percent of their research effort looking into normal economics factors, at most 15 percent of their effort studying political systems, and almost no effort studying culture. If economists really wanted to explain the wealth of nations and not just play math games with fancy models, it looked like they were going to have to start studying culture.
And this conflict between what economists were studying versus what looked truly important showed up in the rest of Sala-i-Martin’s 4 million regressions. Out of the sixty-two possible drivers of growth that Sala-i-Martin looked at, eighteen factors were able to run his mega-gauntlet. How many of the eighteen were cultural? By my count, five: fraction Confucian plus
fraction Muslim (positive factor),
fraction Buddhist (positive factor),
fraction Protestant (negative factor), and
fraction Catholic (negative factor).
Geography and past politics were also reliable predictors of economic performance. Across thousands of statistical horse races, it turned out that having been a former Spanish colony, or being in sub-Saharan Africa or Latin America predicted weaker economic performance. Being further from the equator reliably predicted more prosperity: as the joke goes, this explains why Santa’s Village is the most productive economy in the world. Together, culture, geography, and the distant experience of Spanish colonization added up to half of Sala-i-Martin’s winners, but at the time these factors were consuming far, far less than half of the attention of economists.
Since he published his paper, that has changed. Culture, geography, the shadow of the past—economists study all of these with cross-country comparisons, with laboratory experiments, with theory, and with historical research. This book tells part of that story with a particular focus: I’m going to show how the shadow of the past is transmitted through culture to shape the diverse economies we see around the world today. And I’ll show how a lot of what people think of as just “the power of geography” isn’t that at all—it’s really culture in disguise.
How can I be so sure? Because particularly over the last five centuries, since the great age of European exploration and conquest, people have moved. They’ve moved in their millions from one country to another, sometimes voluntarily, all too often not. And when they moved, they almost never fully assimilated.
1. Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations (London: Printed for W. Strahan and T. Cadell in the Strand, 1776), https://archive.org/details/inquiryintonatur01smit_0.
2. Xavier Sala-i-Martin, “I Just Ran Two Million Regressions.” American Economic Review 87, no. 2 (May 1997): 178–83.