Last year I had an interesting conversation with someone I’ll call the Washington Insider. She asked me why my structural-demographic model predicted rising instability in the USA, probably peaking with a major outbreak of political violence in the 2020s. I started giving the explanation based on the three main forces: popular immiseration, intra-elite competition, and state fragility. But I didn’t get far because she asked me, what immiseration? What are you talking about? We’ve never lived better than today. Global poverty is declining, child mortality is declining, violence is declining. We have access to the level of technology that is miraculous compared to what previous generations had. Just look at the massive data gathered together by Max Rosen, or read Steven Pinker’s books to be impressed with how good things are.

There are three biases that help sustain this rosy view. First, the focus on global issues. But the decrease of poverty in China (which is what drives declining global poverty, because Chinese population is so huge), or the drop in child mortality in Africa, is irrelevant to the working America. People everywhere compare themselves not to some distant places, but to the standard of living they experienced in their parents home. And the majority of American population sees that in many important ways they are worse off than their parents (as we will see below).

Second, the Washington Insider talks to other members of the 1 percent, and to some in the top 10 percent. The top-income segments of the American population have done fabulously in the last decades, thank you very much.

Third, many economic statistics have to be taken with a grain of salt. Government agencies are often under substantial political pressure to put a positive spin on the statistics they publish. Many economists work hard to please the economic elites and other powers-that-be, because that’s how you get ahead in that profession. Fortunately, there are enough “heterodox” economists who provide us with alternative views. This all doesn’t mean that statistics are worse than “damn lies”; on the contrary, one cannot make sense about where we are headed without statistics. The point here is that one needs to understand why different statistics may give us different answers.

So what has been happening with the well-being of common, non-elite Americans? In my work I use three broad measures of well-being: economic, biological (health), and social.

The most common statistics one sees about economic well-being is the trend in per-capita household incomes. This is not a particularly good way to measure economic well-being for two reasons. First, as households became smaller (because Americans have fewer children), the same wage of the primary breadwinner gets divided by a fewer heads, and that yields an illusion of things getting better. Second, as a result of massive entry of women into the labor force, the typical household today has two bread-winners, compared to a single-wage household of fifty years ago. Furthermore, many households today have even more than two wage-earners, because adult children don’t move away. As a result of both of these factors, the time trajectory of household income yields an overly optimistic view of how well Americans are doing economically.

The best way to see the state of working America is to focus on typical wages of non-elite workers (which means excluding CEOs, high earning corporate lawyers, top athletes, and rock stars ). Here’s what real (adjusted for inflation) wages of two typical non-elite groups tell us:

The pattern is unmistakable: rapid, almost linear growth to the late 1970s, stagnation and decline (especially for unskilled labor) thereafter. Here’s a more detailed breakdown of men’s wages since 1979, broken down by wage percentile (10th is the poorest, 95th is the richest):

Source: State of Working America

Why did this happen? I answer this question in a series of posts, Why Real Wages Stopped Growing (see it in Popular Blogs and Series). The TL;DR answer is that it was a combination of immigration, loss of manufacturing jobs overseas, massive entry of women into the labor force (thus, this factor both inflated household income and, perversely, depressed wages for men), and changing attitudes towards labor. A model incorporating these influences does a pretty decent job of capturing both the turning point of the 1970s and fluctuations afterwards:

Source: Ages of Discord

One potential problem with this statistic, real wages, is that one needs to adjust nominal wages for inflation, and a lot of trickery can happen at that stage. This is a big topic (perhaps for a future post). In my work I have sidestepped this issue by focusing on the relative wage, which is the nominal wage divided by GDP per capita, also expressed in nominal dollars. Here what this statistic tells us about the state of working America for the whole history of the US:

Source: Ages of Discord  Relative wage has been declining since the 1960s.

Another important indicator is availability of jobs. The jobless rate published by government agencies is not a very useful statistic, because it tells us about short-term fluctuations, and excludes people who gave up on the job market. A better measure is the labor participation curve, especially for men:

(source)

Dividing men by educational attainment is a way to check that declining labor participation rate is indeed due to decreasing demand for labor (because high school drop-outs have fewer job prospects than college-educated men). The trend is uniformly down, but it is worst for less-educated men.

An amusing way to spin this bad news was pointed out by one commenter on my previous post. An NBER article by Mark Aguiar and Erik Hurst, “Measuring Trends in Leisure”, optimistically concluded that between 1965 and 2003 “leisure for men increased by 6-8 hours per week” and that “this increase in leisure corresponds to roughly an additional 5 to 10 weeks of vacation per year.” A closer reading of the article, however, shows that this “leisure increase” was driven by a decline in “market work hours”. In other words, all those extra 10 percent of men with higher school or less, who dropped out of the work force since 1970, are simply enjoying their “vacations.”

Economic measures of well-being only tell a part of the story. One of the most shocking, to me, developments was that the immiseration since 1970 has affected biological measures of well-being”. Here’s a graph of trends in average stature (height):

Source: Ages of Discord

Panel (a) shows that average stature of native-born Americans grew rapidly until the 1970s, and then stagnated. A real shocker is that for some segments of the population (Black women) it actually declined in absolute terms. Panel (b) shows that there is a clear relationship between economic and biological measures of well-being (it’s further explained in Ages of Discord).

For another health measure, life expectancy, we have a similar situation. Overall, America is losing ground in relative terms (for example, in comparison to robustly growing life expectancies in Western Europe). For some segments of the population the decrease is in absolute terms. Here’s a particularly revealing look at the data:

The correlation between red counties and those who voted for Trump in 2016 is rather obvious.

There has been a lot of discussion recently of factors that may be responsible for declining health of common Americans. I won’t go into it, for lack of space, but here’s one component:

Source

Finally, a good indicator for social well-being is the proportion of Americans married; or, alternatively, the age at which they marry:

Source: Ages of Discord

There is a long-term increase in the age of marriage driven by modernization (top panel), so we are interested in fluctuations around the trend (bottom panel). During the periods of increasing well-being (for example, between 1900 and 1960), average age of marriage tends to drop. Immiseration causes it to rise. In fact, an increasing proportion of people doesn’t marry at all. Many of them stay with their parents, and their earnings help to inflate household income statistics.

We know from the work of Jonathan Haidt and others that one of the most powerful factors explaining personal well-being is social embeddedness. Having a spouse is one of the most fundamental ways of being embedded. But a variety of other indicators, collected by Robert Putnam, shows that Americans are becoming increasingly less connected (I’ve written about it in another post).

In short: a variety of indicators show that well-being of common American has been declining in the last four decades. The technical term for this in the structural-demographic theory is immiseration.

===================================================

Notes

22.VIII.2017: Added the chart on men’s wages since 1979 broken down by wage percentile.

It will soon be two years since the US presidential elections of 2016, which should have made it clear to everybody that our society is in deep crisis. The technical term in the structural-demographic theory (SDT) for it is a revolutionary situation: when the established elites are still holding the levers of power, but the social pressures for crisis have built up to the point where something has to give.

What I found remarkable as we have lived through the past two years (indeed, the past eight years since I made my prediction of the impending crisis), is how precisely we today are following the trajectory into crisis that my colleagues and I saw in the historical societies we have studied. The explanation, probably, is that the three major mechanisms driving up social pressure for crisis in the SDT work in a mutually reinforcing way. The fundamental drive (a kind of a “pump” that drives up social pressure) is the oversupply of labor, which developed after the 1970s as a result of multiple interacting factors, and more recently was made acute by technological change driving automation and robotization. Oversupply of labor is the root cause for both popular immiseration and elite over-production/intra-elite competition. Both of those factors, then, contribute to the fiscal crisis of the state, because immiserated population can’t pay taxes, while the elites work to reduce the taxes on themselves.

We saw all those mechanisms operating in our current crisis. Immiseration of large swaths of the American population was what fueled the successful campaign of a counter-elite presidential candidate, Donald Trump. Intra-elite conflict has reached unprecedented heights (since the First American Civil War), as the established elites are using various means at their disposal to get rid of the counter-elite chief of state. At the same time, a weird coalition of Trump and the established elites (remember, laws must be approved by the Congress) legislates deep cuts into the taxes the elites will pay, bringing the fiscal crisis of the state much sooner. Political violence has also reached new heights, although thankfully mostly demonstrators and counter-demonstrators are beaten up, not killed (a major exception was Charlottesville a year ago).

Until last year I thought that we collectively have a decent chance of avoiding the crisis, but I now have abandoned this hope. A major reason for my pessimism is the resolute refusal by our ruling class (including its both Liberal and Conservative wings) to see the real causes of the crisis. They are internal, not external. As a result, the mid-term elections will be completely free of (largely mythical) Russian influence, but no attempt is made to address the deep structural-demographic causes. All these pressures continue to increase.

The major question on my mind now, instead, is how we could sail through the crisis without a major amount of bloodshed. This is where the “Ginkgo Model” may serve as a useful conceptual device. As I said earlier in this post, the trajectories of entry into structural-demographic crises are fairly narrowly channelized. But once the crisis breaks out, suddenly a much broader fan of possibilities opens up. It’s just like a Ginkgo leaf:

Some post-crisis trajectories go to a really dire territory: a bloody civil war, a revolution bringing an oppressive regime, or disintegration of the state into a number of territorial sections. Other post-crisis trajectories are less dire. In the best scenario, the elites manage to pull together and implement the reforms needed to defuse the pressures for crisis—reversing trends of immiseration and elite overproduction and restoring the fiscal health of the state.

However, unlike in the Ginkgo leaf analogy, the fan of probabilities of emerging from a crisis is heavily lopsided—and unfortunately in favor of really negative outcomes. Over the years I have studied about thirty cases of historical societies going into crisis, and emerging from it, ranging from Rome and China to France, Russia, and the United States. I scored the crisis severity in each by such parameters as the effect on the population (none, mild decline, catastrophic decrease), on the established elites (from mild downward social mobility to dispossession or even extermination), and on the state (territorial fragmentation, external conquest). Adding together these indicators, here’s the result:

As you see, the more positive outcomes (lower severity on the left side) are fairly rare (about 10% of historical cases), while the majority of outcomes cluster in the middle-high severity territory. In fact, this way of presenting outcomes is somewhat misleading, for the following reason. We know that the scale of collective violence in humans, measured by the number of people killed, is not “normally” distributed. Instead, it follows a power law. As an example from my own work, here’s the distribution of the severity of political violence events in the United States between 1780 and 2010:

The frequency distributions of war severity, including both external, inter-state wars and internal, civil wars have the same shape. What it means in non-mathematical terms is that there is no “typical” scale for outcomes of societal crisis. We cannot say that “on average 10,000 people are killed when a civil war breaks out.” The idea of “average” is misleading. A civil war can kill 100, 1000, 10 000, 100 000, 1 000 000 people and even more. The probability of a really severe conflict (e.g., more than 1 mln people killed) is fairly low, but it is much higher than what a naïve person would estimate. This point is admirably discussed by Nassim Nicholas Taleb in The Black Swan and other writings.

What it means for us here in the United States is that the severity of the troubles to come in the next few years to a decade is really impossible to predict. It could be as mild as the late 1960s–early 1970s, with the violent urban riots and a fairly ineffectual terrorist campaign by the Weather Underground. Or it could be as bad as the First American Civil War. Once again, a real catastrophic collapse of our society may not be highly probable, but it is much more probable than we think.

For me the greatest eye-opener in The Rainforest: The Secret to Building the Next Silicon Valley by Victor Hwang and Greg Horowitt was a realization that starting your own innovative company is a deeply irrational decision. Considering the amount of effort (and often your own money) that you are going to invest in it and the very low probability of success, it simply doesn’t make sense to start a company—as long as you measure success in dollars. As the authors write at one point, “the process of launching a startup company has many similarities to riding a roller-coaster. It is a highly irrational act.”

There is useful diagram on p. 224 of The Rainforest explaining the components that have to work in order for the venture to become success:

• Technology works
• Product works
• Customers buy
• Manufacturing works
• Business model works
• Enough capital
• Team can handle this whole thing

There are seven components of success in this scheme, and all of them must work in order for “this whole thing” to succeed. Even if we assign an unrealistically high chance of success—50%—to each step, the probability of all of the seven tosses coming heads up is less than 1%. A more realistic chance of success for each step, like 20%, yields an overall probability of around 0.001 percent. If you want to become a billionaire, there are much more certain routes to wealth—for example, working up the corporate ladder in a well-established large company or running a hedge fund. Or, as Thomas Piketty suggests, inheriting the wealth.

And I am not even talking about the “psychic costs” involved in starting a successful business. It can take many years before you find out whether you succeed, and all that time one must live with the very real possibility of failure, that all that hard work will be, in the end, wasted. At least, if you fail at becoming a CEO of a huge corporation, you will be still very handsomely remunerated along the way and can look forward to a very comfortable nest egg when you retire. Another huge psychic cost is having to ask people for money, and then almost always being turned down. As Hwang and Horowitt write, “successful innovation requires self-sacrifice.”

We have the examples of success in front of our eyes, the Bill Gateses and Steve Jobses, but for each of them there are thousands of entrepreneurs who ruined their lives by trying to launch a firm. Even the brilliant Nikola Tesla died in poverty, weighed down by debt (at the end of his life, he lived in a series of New York hotels, leaving behind unpaid bills).

Curiously enough, I have a great personal understanding of the costs of entrepreneurship, because launching Seshat: Global History Databank was in all respects, except one, an identical experience to an investor launching a successful business. The one difference, of course, is that the success of Seshat is measured not in dollars, but in the number of publications in top journals, the number of citations by other researchers, the theories that we either confirm or reject, and our increased understanding of how human societies evolve and function.

So, what does actually motivate entrepreneurs? Some of them are in it just for the money, and most of those fail. Successful ones are typically motivated by extrarational reasons: “thrill of competition, human altruism, a thirst for adventure, a joy of discovery and creativity, a concern for future generations, and a desire for meaning in one’s life.”

Even more eye-opening was the new understanding of venture capitalists that I gained after reading The Rainforest. I used to refer to them as “vulture capitalists”, but I now understand that it was unfair, especially when talking about successful VCs. Because successful VCs, as The Rainforest explains, also cannot be motivated by greed alone. The problem is, again, the high probability of failure. It is simply not rational to invest in a startup at the very early stage. Usually VCs will know that the first hurdle (“technology works”) has been successfully passed. But the remaining six hurdles still need to be surmounted. One way of increasing the payoff in case the venture succeeds is to force the entrepreneur to yield a large percentage of equity in the company. But that’s self-defeating, because that destroys the motivation of the entrepreneur to work hard.

At a later stage, once most of the hurdles have been cleared, it makes perfect sense to invest in a startup, but how do they get to that stage? It takes either investors behaving extrarationally, being motivated by the same values cited above, or collective action channeled by governments—in fact, it takes all of that.

Most venture funds that help entrepreneurs during the early stages are funded by governments who hire VCs to run them, or by VCs who venture their own money in parallel with a government fund. I had some experience of this recently, when one of the Seshat directors, Kevin Feeney, launched a new company, Data Chemist (read about it in The Irish Times).

I end my review of Hwang and Horowitt’s book with this quote “Rainforests depend on people not behaving like rational actors.” It requires extrarational motivations of all the key players, including the inventors and investors, and also governments. I am not saying that all business is like this. From what I read in the press, the world of financial organizations, large corporations, and corporate law seems to be driven largely, or entirely by greed. Branko Milanovic is right in that. But not all business is like that. Innovation is really the key to why we live better than people did one hundred, or one thousand years ago. And that business requires extrarational motivations, self-sacrifice, and cooperation.

One of the chief reasons I became an advocate of the Cultural Multilevel Selection (CMLS) theory is that it wonderfully clarifies the relationship between competition and cooperation.

• Competition between groups (up to whole societies) fosters within-group cooperation.
• Competition within groups (between their members) destroys cooperation.

If you wanted a two-sentence summary of Ultrasociety this would be it.

I’ve been applying these principles in my research on the evolution of complex societies (and a lot more is to come, now that the Seshat Databank have come of age and is generating a tremendous volume of empirical results—which you will see in a year or two, as academic publication mill-stones grind sooo slowly).

But I have also felt that the CMLS theory tells us a lot not only about political organizations like the states, but also about economic organizations—firms competing in markets. This goes very much against the grain, especially as far as economists are concerned. Milton Friedman, of course, always argued that economic agents should strictly follow their material interests; there is no place for “extra-rational motives” in business. Most economists today feel the same way, although few are willing to state it as boldly as Friedman did.

Thus, it was very refreshing to receive two years ago an email from Branko Milanovic, an economist whom I greatly admire, in which he was willing to go on record and state this position very forcefully (I published Branko’s letter as a guest blog on Cliodynamica). I also invited two economist friends to comment: Bob Frank and Herb Gintis, as well as writing a response myself. The whole exchange was recently re-published by Evonomics and generated a lot of discussion.

More recently Branko wrote a review of Kate Raworth’s new book Doughnut Economics. A central question in the ensuing debate between him and Kate (see it here) is the same we debated two years ago (see also a good summary on Vulgar Economics). Here’s what Branko wrote about the “human nature under hyper-commercialized global capitalism”:

Here I respectfully decline to be moved by the results of any of the “games” that Kate cites and that are supposed to reveal human nature. These games are indeed games; they are not the way people behave in real life. Games are good in generating publishable papers but they tell us nothing about how the same people would (or do) behave in real life.

Two years ago I wouldn’t know how to respond to Branko on this point, but fortunately recently I finished reading a remarkable book, which provides me all the ammunition I need.

Before discussing it, let’s define the question more explicitly. I agree that most people much of the time, and some people all of the time, are motivated by very “rational” calculations. When I decide which supermarket I am going to go for food, I try to minimize the amount of money I will pay and the amount of time I will spend, while maximizing produce quality and selection. It’s a very straightforward optimization problem.

But capitalism is not just about buying and selling things—people have been doing commerce for millennia before capitalism. Surely the amazing capacity of capitalism to transform knowledge into innovation, and innovation into economic growth is one of the central of its attributes? So let’s talk about such successful innovation hotspots, as the Silicon Valley. What are the motivations driving successful entrepreneurs within such hotspots?

If you want to find out the answers to these questions, read The Rainforest: The Secret to Building the Next Silicon Valley by Victor Hwang and Greg Horowitt. Now, Hwang and Horowitt are not scientists, and their book is not a rigorous scientific study. But they spent decades bringing together venture capitalists and entrepreneurs, both in their native California and all over the globe—“Japan, Taiwan, Scandinavia, and New Zealand, … Mexico, Egypt, Kazakhstan, Colombia, Saudi Arabia, and the Palestinian Territories.” Given their enormous experience, the empirical base, with which they operate in the book, transcends the dismissive academic characterization as “anecdotal.”

A central theme that recurs throughout the book is that successful entrepreneurs, and the successful innovation systems in which they operate, such as the Silicon Valley or Route 128 in Massachusetts, are the antithesis of the rational businessperson postulated by Branko, one who is solely motivated by money. In fact, “Rainforests [their term for successful innovation systems] depend on people not behaving like rational actors.” “For Rainforests to be sustainable, greed must be restrained.” “Predatory venture capitalists might win a few in the short run, but they do not last long in the business and are unable to build lasting firms.”

The evolutionary logic of entrepreneurship, according to Hwang and Horowitt, is precisely the opposite of that posited by Branko. Predatory, super-competitive individuals and firms are eliminated by natural selection, and only cooperative ones survive. They write:

Extra-rational motivations—those that transcend the classical divide between rational and irrational—are not normally considered critical drivers of economic value-creation. …  These motivations include the thrill of competition, human altruism, a thirst for adventure, a joy of discovery and creativity, a concern for future generations, and a desire for meaning in one’s life, among many others. Our work over the years has led us to conclude that these types of motivations are not just “nice to have.” They are, in fact, “must have” building blocks of the Rainforest.

Many successful entrepreneurs (think Steve Jobs or Elon Musk) clearly were not motivated solely by money. Naturally, they did not give their billions away, and for some other very successful innovators, perhaps, all they wanted was to become filthy rich. But the point that Branko makes, that capitalism “is a system really built on the best use of our vices, including greed” is clearly wrong.

This is why when governments and corporations try to incentivize innovation by focusing on financial mechanisms, the overall result is failure. By the end of the book, Hwang and Horowitt boil down their own recommendations as to what makes successful “Rainforests” thrive. They are four.

First, diversity, which brings people with very different knowledge and skills together, such as a scientist, a venture capitalist, an engineer, a sales specialist, and an administrator (a CEO).

Second, extra-rational motivations, because self-regarding rational actors are simply unable to cooperate to launch a successful innovation enterprise.

Third, social trust, because successful cooperation is the only way to beat the terrible odds against a successful innovation startup, and cooperation requires trust.

Fourth, a set of social norms that regulate the behavior of various cooperating agents, and willingness both to follow them and to enforce these rules by various sanctions.

In other words, Hwang and Horowitt describe a system that uses precisely the same components to bring about cooperation that have been studied in other settings (a foraging group, a military troop, a religious sect, and a state), and in the abstract, by cultural evolutionists.

The Rainforest, then, provides ample empirical material to reject the theory that economic growth, which is based on innovation, is moved by self-interested rational agents. But—and it was one of the real eye-openers for me—it also explains why this is so. I discuss this in Part II.

I have traveled extensively in southern Africa over the past 25 years—South Africa, Lesotho, Botswana, Zambia, Malawi, and now Namibia. But in all my previous trips I was lucky to see a black rhino only once (in South Africa’s Hluhluwe-iMfolozi Park). The problem, of course, is that by 1995 the world-wide population of the black rhino had collapsed to less than 2,500. From that low point, the species made a tremendous comeback, more than doubling in numbers. And Namibia is probably the best country to reliably see rhinos (both black and white). On a night drive in the Etosha Pan Park a week ago I saw seven rhinos within a span of one hour: first, two black rhinos at a waterhole, then a mother and a baby white rhino at another waterhole, and a family group of three white rhinos by the road we drove on. As it was at night, I don’t have good pictures of these encounters.

Fortunately for a photographer in me, I saw another black rhino in the broad daylight, during a game drive in the Waterberg Plateau Park. As the name indicates, the park is situated on a high plateau (all photos in this post are ©PeterTurchin):

It’s well protected from poachers, so that the population of black rhino there has become a source of rhinos used in re-introductions to areas where rhino were driven to extinction.

Waterberg Park has an ingenious system of blinds, situated next to waterholes, which can be used by humans to spy on wildlife. At one of these blinds we were watching a group of buffalo drinking water and licking salt:

when a fine specimen of black rhino entered stage left:

The first thing he did was mark the territory by spraying urine on the soil and then kicking it back to spread the scent (which is how we knew it was a male):

After that, the rhino’s interactions with the rest of animals were fairly amicable, although everybody (even large buffalo males) gave way when the rhino visited the water hole.

Incidentally, the antelope in the background is the eland, the largest antelope of them all:

Having drunk its fill, the rhino proceeded to the salt lick. On this photo you can see him “licking his chops” (note the tongue):

One of the features of game viewing in Namibia is that the country is very dry, and during the dry season (which is most of the year, anyway) the game congregates around water holes, making it easy to see them. Another notable interaction at a waterhole that we saw some days before in Etosha Pan was an altercation between an elephant and  hyenas.

WARNING: a few of the photos below can be considered as quite gruesome! Proceed at your risk.

What happened was that two days before we arrived at the Namutoni Camp in eastern Etosha two lions brought down a giraffe at a waterhole nearby. Alas, by the time we got to the kill, the lions had already departed. But there were plenty of scavengers, vultures and hyenas (and an odd jackal):

Here’s a hyena working on a piece of giraffe, while the head of the poor giraffe looks melancholically on:

The hyenas really trashed the waterhole, dropping rotting pieces of the giraffe in it and defecating. When an elephant showed up for a drink, he was really incensed at such unhygienic habits:

So the hyena decided to drag away what was left of the giraffe’s leg to be enjoyed in privacy, while being chased by the angry elephant:

I can sit for hours watching animals do their thing.

As a boy growing up in Russia I read a bunch of adventure stories about intrepid explorers traveling to far-off places. In one book a band of adventurers voyaged to the Skeleton Coast to search for diamonds. So one might say that it’s been a long time that I dreamed of visiting this remote and dangerous territory. Last week I finally realized the dream.

Over the past week I have been traveling in Namibia with two friends. It’s a spectacular country, justly famous for its landscapes and wildlife.

Our trek up the Skeleton Coast started at sunrise in the quaint town of Swakopmund (all photographs in this blog ©PeterTurchin):

Swakopmund in the early morning

Very quickly all signs of civilization are left behind, and all that one sees is a cold (water temperature is 15°C) and turbulent sea on the left hand side, and a desert on the right side. The road is basically sand cemented by salt:

The road

The road quality actually was quite good. According to reports, it becomes slick and treacherous after a rain, but it hardly ever rains on the Skeleton Coast.

Sand and salt are everywhere; in fact, there are a number of salt-production businesses along the road:

Salt

The Skeleton Coast got its name because it is an incredibly dangerous area for navigation. It is literally littered with shipwrecks, from recent ones:

A recent shipwreck

To older ones, which provide excellent nesting grounds for sea birds:

An older wreck

And then to ancient wrecks, that have sunk into the sand:

An ancient wreck

In addition to these “skeletons” of ships, the sand is littered with actual skeletons:

The Skeleton Coast, true to its name

The road ends at an isolated outpost of Terrace Bay:

Terrace Bay

which doesn’t have a permanent population (people come to work there for three weeks, and then take a week off with their families back home).

The Skeleton Coast is not quite as devoid of life as it looks. Driving out of Terrace Bay the next morning we saw a couple of jackals:

A jackal

All in all, traveling in this remote and desolate land was a wonderful adventure. Unlike the heroes of my childhood books, I didn’t dig for diamonds in the sand. But if you want to do it, apparently you can:

In the first part of my critique of The Dictator’s Handbook: Why Bad Behavior Is Almost Always Good Politics by Bruce Bueno de Mesquita and Alastair Smith (BDM&S), I slammed the theoretical foundations of their argument. The book also has a lot of empirical content, and in Part II I want to talk about that.

The basic approach taken in the book is anecdotal. It’s not a substitute for rigorous analysis because of the ever-present danger of “cherry-picking”—selecting only the anecdotes that support one’s pet theory, while studiously ignoring any counter-examples (see also Technical Note at the end). But a series of well chosen and well presented illustrations of the theoretical ideas in the book from history, politics, and business could make for an enjoyable reading.

Unfortunately, BdM&S selection of examples exhibits an extreme form of cherry-picking, in fact even going beyond that, when they make out examples supporting their theory “from whole cloth.” For example, did you know what was the cause of the Russian Revolution? It turns out that Czar Nicholas II “foolishly cut the income from one of his major sources of revenue, the vodka tax, at the same time that he fought World War I. … With vodka banned, his revenue diminished sharply. .. Soon Nicholas was no longer able to buy loyalty. As a result, his army refused to stop strikers and protesters.” This is a remarkable story; unfortunately it has nothing to do with the real causes of the Russian Revolution (if you are interested in details, see Chapter 10 of our Secular Cycles).

Another new historical fact that I gleaned from The Dictator’s Handbook  was that “Kerensky’s revolutionaries were able to storm the Winter Palace in February 1917.” Of course, it was not “Kerensky’s revolutionaries” but Lenin’s Bolsheviks, and the storm of the Winter Palace was not in February, but in October (old style). If the authors know history so poorly, why didn’t they employ a fact-checker?

These are just two examples out of many more. But more important is the extreme form of cherry picking that BdM&S practice in The Dictator’s Handbook. They only give examples of leaders behaving corruptly (by the way, this is the same criticism that Frederick the Great of Prussia leveled against Machiavelli in Anti-Machiavel).

Frederick the Great of Prussia as Crown Prince (1739), about the same time when he wrote Anti-Machiavel. Source

The very first example with which the book starts deals with one Robert Rizzo, who was city manager of Bell, California. There is no question that Bell was an extremely corrupt individual. In fact, he was the highest paid city manager in the entire US! I think it wouldn’t be unwarranted to conclude that Rizzo was in the top 1 percent, or even top 0.1 percent of the most corrupt American city managers. But what about the rest of them?

Remember that the main postulate of the BdM&S theory is that all politicians (as well as all business leaders; in fact all people) are only “motivated to do what is good for them, not what is good for others.”  To illustrate this general idea with an individual selected from the top 1 percent of the corruption distribution, and not to balance it with a discussion of how typical this behavior is, is intellectually dishonest.

Now, I am not a starry eyed idealist. I know full well that there is plenty of corruption and self-dealing in our Republic. There are lots of egotistical people. It’s even quite possible that there is a selection process that ensures that the fraction of egomaniacs and narcissists among the political and business leaders increases as one goes up the hierarchy (although I’d like to see some data on that). Nevertheless, not all leaders are like that.

As an example, let’s consider such obvious example of prosocial behavior as volunteering for the army when your country is at war. Of course, George W. Bush and Bill Clinton would be fine examples for the BdM&S theory. But think about the previous generation, which Robert Putnam called the Long Civic Generation. Jack Kennedy fought in World War II, and his older brother, Joseph Jr, was killed in action in 1944.

POF/PSF/PT109-1 Lt.(jg) John F. Kennedy aboard the PT-109 in the South Pacific, 1943. Photograph in the John F. Kennedy Presidential Library and Museum, Boston.

George W.’s father, George H.W. Bush, was a naval aviator whose plane was shot down by the Japanese, also in 1944.

Shipmates of the submarine USS Finback rescue Bush Source

I return to my main critical point: people are different–some selfish others prosocial; and so are  politicians. It is not surprising that The Dictator’s Handbook is so popular—our current generation of politicians may easily be the most miserable one in American history (although Gilded Age politicians could have been even more corrupt). What we need is a theory that would help us understand why there is variation between leaders, and why there is change with time: some generations of leaders behave more prosocially, others are more corrupt. Due to its theoretical and empirical flaws, BdM&S’s book does not advance us towards such an understanding.

Technical Note: Bueno de Mesquita and colleagues have also published a 2003 volume, The Logic of Political Survival. That book, among other things, presents a statistical analysis of predictions from the selectorate theory with Polity IV data. Unfortunately, their analysis suffers from fatal flaws, in particular, because they used a bizarre residualization procedure. In an article published in American Political Science Review, Kevin Clarke and Randall Stone show that this procedure leads to omitted variable bias. When the data are reanalyzed properly, as Clarke and Stone did, most of their important findings don’t survive.

One of my long-term interests is in the dynamics of leader-followers systems. Large-scale societies and other large groupings of people (including corporations) cannot be purely egalitarian. As I’ve written in another post, humans are not ants.

We must have leaders to organize large-scale cooperation. Inevitably, there will be elites (in the neutral sociological sense: simply a small proportion of the population who concentrates social power in their hands) and commoners (the rest of the population). The big question is how do (some) human organizations avoid, or mitigate (to a greater or lesser degree) the iron law of oligarchy – one of the most fundamental sociological laws (put simply, power corrupts).

Thus I looked to reading The Dictator’s Handbook: Why Bad Behavior Is Almost Always Good Politics by Bruce Bueno de Mesquita and Alastair Smith with great anticipation. I had an inkling that I would disagree on much with the authors, but I was looking forward not to agree, but to learn.

I was mistaken. The book fails, and fails badly, on both theoretical and empirical grounds. It’s so bad, I almost decided not to review it. However, it has been enormously successful. It sold a lot of copies, and garnered more than 200 reviews on the Amazon, most of them glowingly positive (average rating 4.6 out of 5). It also inspired a very popular info-video by CGP Grey (over 6 million views).

Thus, I think it becomes my public duty to explain why the book is bad.

One of the few points in the book, with which I agree, is that our job as social scientists is to study how the world really works, not how we wish it worked, or as BdM&S say in the beginning of the book, “the world can only be improved if first we understand how it works and why.”

In the last chapter, the authors say, “After the past nine chapters of our cynical—but we fear accurate—portrayal of politics…” Cynical, yes. Accurate? Far from it.

The theory proposed by BdM&S is a very simple, even naïve, version of the rational-choice model so favored by economists and political scientists in the twentieth century. As the authors say early on in the book, “politics, like all of life, is about individuals, each motivated to do what is good for them, not what is good for others.”

It’s as though the book was written not in 2011, but thirty years ago, before the massive tsunami of evidence showing that this is not true at all.

People are different. Some (20-30% in most large-scale societies) are indeed pure rational actors who only maximize their personal utility expressed in purely materialistic terms. But the majority of population is motivated by additional considerations: desire to do good to others or for the society, loyalty, friendship, honor, sacred values, and many more.

The theory propounded by BdM&S, then, is pure Machiavelli, which they acknowledge by quoting him approvingly. Anyone seeking to become a ruler must give followers “castles and possessions, as well as money and subjects; so that surrounded by these he may be able to maintain his power, and that by his support they may satisfy their ambitions.”

I have debunked this theory at length in War and Peace and War, so I won’t do it here. What is startling is to see this bankrupt theory pushed so vigorously by seemingly competent academics. How could you possibly ignore I don’t know how many thousands of articles in experimental economics that have swept away the naïve, stripped-down version of the rational-choice theory?

You probably think I have presented a caricature of BdM&S’s theory. Not at all! Check the book, or watch CGP Grey’s info-video, which is a very accurate statement of what the book says. Here’s another direct quote: “Paying supporters, not good governance or representing the general will, is the essence of ruling.”

Just about the only elaboration of Machiavelli is BdM&S’s division of followers (a population of a country, or people working for an organization) into essentials, influentials, and interchangeables. If you are interested, read about it in this Wikipedia article—I don’t see the point of discussing it as the overall theory doesn’t make sense to me, because of its bankrupt model of human nature.

Now, I would be the first one to admit that there are a lot of dictators, democratic politicians, CEOs of big corporations, and even leaders of ostensibly charitable organizations who are reasonably well described by the Machiavelli model. But not all leaders are like that. We know empirically that leaders are a mixed lot. Some, like Idi Amin, are really close to the Machiavelli end, while a few are closer to—let’s say—the Gandhi/Mandela end. And most are in between. Why do we see such heterogeneity is a very interesting question, and I will talk about it in Part II.

Part II here

The last installment in this series (first one here) adopts a more critical stance towards the article by McConnell et al., Lead pollution recorded in Greenland ice indicates European emissions tracked plagues, wars, and imperial expansion during antiquity. As I said in the second post, the only quantitative part of this study was coming up with the lead pollution curve, while all the comparisons between the lead curve and historical events in Rome and elsewhere in the Western Mediterranean were qualitative (with silver content of the Roman denarius the only exception). Here’s the relevant figure:

Figure 3 of McConnell et al. 2018

The problem with an “eyeball” comparison between a quantitative curve and a set of qualitative events is the danger of confirmation bias. Or put simply, cherry-picking of data. It’s way too easy to find an explanation for any uptick and down-tick in the curve when one has a large list of events to choose from. McConnell et al. appear to be guilty of this.

One of the major messages of the article is that wars in the Western Mediterranean, and especially those affecting the Iberian Peninsula, have a depressing effect on silver production (which lead emissions proxy). But the authors also use the opposite effect to explain one of the upticks:

Longer-term declines possibly were linked to disincentives to investment in war-torn regions. For example, lead emissions dropped notably at the outbreak of the first Punic War (264–241 BCE) but rose in the later years as Carthage increased its minting of silver coin to pay mercenaries.

What we need is an objective quantitative method to test the hypothesis of a negative correlation between silver production and warfare. Fortunately, there is such a proxy.

A hoard of Roman coins

I have literally tons of data on the incidence of coin hoards. It turns out that the overwhelming majority of the hoards we find in modern times are “emergency hoards.” These are buried stores of wealth that people hide during times of danger. And then if something terrible happened to them–they are killed, or enslaved, or driven into exile–the hoards are not recovered by the original owners. Thus, the fluctuations in the frequency of coin hoards per decade provide us with a very useful proxy for the intensity of warfare.

Some years ago Walter Scheidel and I published an article (also in PNAS) which capitalized on this relationship to resolve a long-standing debate in Roman demography. Here I plot the data from that article on coin hoard frequency and and index of war intensity in Italy, derived from textual sources:

The three periods of intense warfare that are prominent in this figure are the Second Punic War and the two rounds of civil wars during the crisis of the Roman Republic (for details, see Chapter 6 of Secular Cycles). So, what happens when we compare the hoard index of war intensity with the lead emissions index? Here’s what:

A correlation coefficient between these two curves, equaling a measly -0.09, confirms that there is no apparent relationship here.

Could it be because the hoards come from Italy, while the hypothesis is that it is warfare in Spain that should depress silver production? We are fortunate to have an Inventory of Greek Coin Hoards that covers all of Mediterranean, and beyond, published by Margaret Thompson and colleagues in 1973. Focusing on the Western Mediterranean, we see the following pattern:

These curves decline shortly after 200 BCE, because the Roman denarius becomes the main means of exchange in the Western Mediterranean (note that Italian Hoards above are largely based on the silver denarius, with a few bronze coin hoards early in the sequence).

Let’s compare the total hoards in West Mediterranean and just hoards in Spain to the lead emissions curve:

The correlation coefficients between the lead curve and either of the hoard curves is actually slightly positive. Thus, I conclude that the hypothesis of McConnell and co-authors, that warfare corresponds to downturns in lead emissions, is not supported.