Tag Archives: Inequality

The One Ruler Obsession

I often teach undergraduate law & economics. Sometimes the first paper I assign is to suggest property rules to deal with conflicts regarding asteroids, orbits, and sunlight in the solar system, in the future when there’s substantial activity out there. This feels to students like a complex different situation, and in fact few understand even the basic issues.

Given just two pages to make their case, a large fraction of students (~1/3?) express fear that one person or organization will take over the entire solar system, unless property rules are designed to explicitly prevent that. And a similar fraction suggest the “property rule” of having a single government agency answer all questions. Whatever question or dispute you have, fill out a form, and the agency will decide.

Yet in my lectures I talk a lot about concepts and issues of property rights, but never mention government agency issues or scenarios, nor the scenario of one power taking over everything. And econ undergrads at my school are famous for being relatively libertarian.

I conclude that most people have a strong innate fear of power concentrations, and yet also see the creation of a single central power as an attractive general solution to complicated problems. I’ve seen the same sort of thing with a great many futuristic tech and policy issues. Whatever the question, if it seems complicated, most people are concerned about inequality, especially that it might be taken to the max, and yet they also like the idea of creating a central government-like power to deal with it.

I’ve certainly seen this in concerns about future rampaging robots (= “AI risk”). Many, perhaps most, people express concerns that one AI could take over everything, and many also like the “solution” of one good AI taking over everything.

I recently came across similar reasoning by Frederick Engels back in 1844, in his Outlines of a Critique of Political Economy. Having seen the early industrial revolution, not understanding it well, but fearing where it might lead, Engels claims that the natural outcome is extreme concentration of power. And his solution is to create a different central power (e.g., communism). Of course while there was some increase in inequality and concentration, it wasn’t remotely as bad as Engels feared, except where his words helped to inspire the creation of such concentration. Here is Engels:

Thus, competition sets capital against capital, labour against labour, landed property against landed property; and likewise each of these elements against the other two. In the struggle the stronger wins; and in order to predict the outcome of the struggle, we shall have to investigate the strength of the contestants. First of all, labour is weaker than either landed property or capital, for the worker must work to live, whilst the landowner can live on his rent, and the capitalist on his interest, or, if the need arises, on his capital or on capitalised property in land. The result is that only the very barest necessities, the mere means of subsistence, fall to the lot of labour; whilst the largest part of the products is shared between capital and landed property. Moreover, the stronger worker drives the weaker out of the market, just as larger capital drives out smaller capital, and larger landed property drives out smaller landed property. Practice confirms this conclusion. The advantages which the larger manufacturer and merchant enjoy over the smaller, and the big landowner over the owner of a single acre, are well known. The result is that already under ordinary conditions, in accordance with the law of the stronger, large capital and large landed property swallow small capital and small landed property – i.e., centralisation of property. In crises of trade and agriculture, this centralisation proceeds much more rapidly.

In general large property increases much more rapidly than small property, since a much smaller portion is deducted from its proceeds as property-expenses. This law of the centralisation of private property is as immanent in private property as all the others. The middle classes must increasingly disappear until the world is divided into millionaires and paupers, into large landowners and poor farm labourers. All the laws, all the dividing of landed property, all the possible splitting-up of capital, are of no avail: this result must and will come, unless it is anticipated by a total transformation of social conditions, a fusion of opposed interests, an abolition of private property.

Free competition, the keyword of our present-day economists, is an impossibility. Monopoly at least intended to protect the consumer against fraud, even if it could not in fact do so. The abolition of monopoly, however, opens the door wide to fraud. You say that competition carries with it the remedy for fraud, since no one will buy bad articles. But that means that everyone has to be an expert in every article, which is impossible. Hence the necessity for monopoly, which many articles in fact reveal. Pharmacies, etc., must have a monopoly. And the most important article – money – requires a monopoly most of all. Whenever the circulating medium has ceased to be a state monopoly it has invariably produced a trade crisis; and the English economists, Dr. Wade among them, do concede in this case the necessity for monopoly. But monopoly is no protection against counterfeit money. One can take one’s stand on either side of the question: the one is as difficult as the other. Monopoly produces free competition, and the latter, in turn, produces monopoly. Therefore both must fall, and these difficulties must be resolved through the transcendence of the principle which gives rise to them. (more)

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Boost For Being Best

The fraction of a normal distribution that is six or more standard deviations above the mean is one in ten billion. But the world has almost eight billion people in it. So in principle we should be able to get six standard deviations in performance gain by selecting the world’s best person at something, compared to using an average person.

I’m revising Age of Em for a paperback edition, expected in April. The rest of this post is from a draft of new text elaborating that point, and its implication for em leisure:

Em workers also earn wage premiums when they are the very best in the world at what they do. Even under the most severe wage competition, a best em can earn an extra wage equal to the difference between their productivity and the productivity of the second best em. When clans coordinate internally on wage negotiations, this is the difference in productivity between clans. (Clans who can’t coordinate internally are selected out of the em world, as they don’t cover their fixed costs, such as for training and marketing.)

Out of 10 billion independently and normally distributed (IID) samples, the maximum is on average about 6.4 standard deviations above the mean. Average spacings between the second, third, fourth highest samples are roughly 0.147, 0.075, and 0.05 standard deviations respectively (Branwen 2017). So when ems are selected out of 10 billion humans, the best em clan may be this much better than other em clans on normally distributed parameters. Using the log-normal wage distribution observed in our world (Provenzano 2015), this predicts that the best human in the world at any particular task is four to five times more productive than the median person, is over three percent more productive than the second most productive person, and is five percent more productive than the third most productive person.

If em clan relative productivity is drawn from this same distribution, if maximum em productivity comes at a 70 hour workweek, and if the best and second best em clans do not coordinate on wages they accept, then even under the strongest wage competition between clans, the best clan could take an extra 20 minutes a day more leisure, or two minutes per work hour, in addition to the six minutes per hour and other work breaks they take to be maximally productive.

This 20 minute figure is an underestimate for four reasons. First, the effective sample size of ems is smaller due to age limits on desirable ems. Second, most parameters are distributed so that the tails are thicker than in the normal distribution (Reed and Jorgensen 2004).

Third, differing wealth effects may add to differing productivity effects. On average over the last 11 years, the five richest people on Earth have each been about 10 percent richer than the next richest person. If future em income ratios were like this current wealth ratio, then the best em worker could afford roughly an extra hour per day of leisure, or an additional six minutes per hour.

Fourth, competition probably does not take the strongest possible form, and the best few ems can probably coordinate to some extent. For example, if the best two em clans coordinate completely on wages, but compete strongly with the third best clan, then instead of the best and second best taking 20 and zero minutes of extra leisure per day, they could take 30 and 10 extra minutes, respectively.

Plausibly then, the best em workers can afford to take an additional two to six minutes of leisure per hour of work in a ten hour work day, in addition to the over six minutes per hour of break needed for maximum productivity.

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Baum on Age of Em

In the Journal Futures, Seth Baum gives the first academic review of Age of Em. First, some words of praise: Continue reading "Baum on Age of Em" »

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Big Software Firm Bleg

I haven’t yet posted much on AI as Software. But now I’ll say more, as I want to ask a question.

Someday ems may replace humans in most jobs, and my first book talks about how that might change many things. But whether or not ems are the first kind of software to replace humans wholesale in jobs, eventually non-em software may plausibly do this. Such software would replace ems if ems came first, but if not then such software would directly replace humans.

Many people suggest, implicitly or explicitly, that non-em software that takes over most jobs will differ in big ways from the software that we’ve seen over the last seventy years. But they are rarely clear on what exact differences they foresee. So the plan of my project is to just assume our past software experience is a good guide to future software. That is, to predict the future, one may 1) assume current distributions of software features will continue, or 2) project past feature trends into future changes, or 3) combine past software feature correlations with other ways we expect the future to differ.

This effort may encourage others to better clarify how they think future software will differ, and help us to estimate the consequences of such assumptions. It may also help us to more directly understand a software-dominated future, if there are many ways that future software won’t greatly change.

Today, each industry makes a kind of stuff (product or service) we want, or a kind of stuff that helps other industries to make stuff. But while such industries are often dominated by a small number of firms, the economy as a whole is not so dominated. This is mainly because there are so many different industries, and firms suffer when they try to participate in too many industries. Will this lack of concentration continue into a software dominated future?

Today each industry gets a lot of help from humans, and each industry helps to train its humans to better help that industry. In addition, a few special industries, such as schooling and parenting, change humans in more general ways, to help better in a wide range of industries. In a software dominated future, humans are replaced by software, and the schooling and parenting industries are replaced by a general software industry. Industry-independent development of software would happen in the general software industry, while specific adaptations for particular industries would happen within those industries.

If so, the new degree of producer concentration depends on two key factors: what fraction of software development is general as opposed to industry-specific, and how concentrated is this general software industry. Regarding this second factor, it is noteworthy that we now see some pretty big players in the software industry, such as Google, Apple, and Microsoft. And so a key question is the source of this concentration. That is, what exactly are the key advantages of big firms in today’s software market?

There are many possibilities, including patent pools and network effects among customers of key products. Another possibility, however, is one where I expect many of my readers to have relevant personal experience: scale economies in software production. Hence this bleg – a blog post asking a question.

If you are an experienced software professional who has worked both at a big software firm and also in other places, my key question for you is: by how much was your productive efficiency as a software developer increased (or decreased) due to working at a big software firm?  That is, how much more could you get done there that wasn’t attributable to having a bigger budget to do more, or to paying more for better people, tools, or resources. Instead, I’m looking for the net increase (or decrease) in your output due to software tools, resources, security, oversight, rules, or collaborators that are more feasible and hence more common at larger firms. Ideally you answer will be in the form of a percentage, such as “I seem to be 10% more productive working at a big software firm.”

Added 3:45p: I meant “productivity” in the economic sense of the inputs required to produce a given output, holding constant the specific kind of output produced. So this kind of productivity should ignore the number of users of the software, and the revenue gained per user. But if big vs small firms tend to make different kinds of software, which have different costs to make, those differences should be taken into account. For example, one should correct for needing more man-hours to add a line of code in a larger system, or in a more secure or reliable system.

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The Elephant in the Brain

One of the most frustrating things about writing physical books is the long time delays. It has been 17 months since I mentioned my upcoming book here, and now, 8.5 months after we submitted the full book for review, & over 4 months after 7 out of 7 referees said “great book, as it is”, I can finally announce that The Elephant in the Brain: Hidden Motives in Everyday Life, coauthored with Kevin Simler, will officially be published January 1, 2018. Sigh. See summary & detailed outline at the book’s website.

A related sad fact is that the usual book publicity equilibrium adds to intellectual inequality. Since most readers want to read books about which they’ve heard much publicity lately from multiple sources, publishers try to concentrate publicity into a narrow time period around the official publication date. Which makes sense.

But to create that burst of publicity, one must circulate the book well in advance privately among “thought leaders”, who might blurb or review it, invite the authors to talk on it, or recommend it to others who might do these things. So people who plausibly fit these descriptions get to read such books long before others. This lets early readers seem to be wise judges of future popular talk directions. Not because they actually have better judgement, but because they get inside info.

Alas, I’m stuck in this same equilibrium. I have a full copy of my final book, except for minor copy-editing changes, and I can share it privately with possible publicity helpers. And when the relative cost to send an email is small relative to possible gains, a small chance may be enough. I’ll also give in to some requests based on friendship or prior help given me (as on my last book), especially when combined with promises to buy the book when it comes out.

But just as grading is the worst part of teaching, I hate being put in the role of bouncer, deciding who is cool enough to be let into my book club, or who has enough favors to trade. At least when teaching I’m expert in whatever topic I’m grading. But here I’m much less expert on deciding who can help book publicity. I’d really prefer the intellectual world to be more of an open competition without favoritism for those with inside connections. But here I am, forced to play favorites.

These are a few of the prices one pays today to publish books. But still, books remain an unparalleled way to call attention to ideas that need more space to explain than an article can offer. And for a relatively unknown author, established publishers still offer more attention than you could generate on your own. But maybe, just maybe, I can do something different with my third book, whatever that may be on.

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Cycles of War & Empire

I’ve just read five of Peter Turchin’s books: Historical Dynamics (2003), War & Peace & War (2006), Secular Cycles (2009), Ultra Society (2015), and Ages of Discord (2016). Four of them in the last week. I did this because I love careful big picture thinking, and Turchin is one of the few who does this now on the big question of historical cycles of conflict and empire. While historians today tend to dislike this sort of analysis, Turchin defies them, in part because he’s officially a biologist. I bow to honor his just defiance and careful efforts.

Turchin’s main story is a modest variation on related farmer-era historical cycle stories, such as by Jack Goldstone in 1991, & Ibn Khaldun in 1377 (!):

Different groups have different degrees of cooperation .. cohesiveness and solidarity. .. Groups with high [cohesion] arise on .. frontier .. area where an imperial boundary coincides with a fault line between two [ethnic] communities .. places where between group competition is very intense. .. Only groups possessing high levels of [cohesion] can construct large empires. ..

Stability and internal peace bring prosperity, and prosperity causes population increase .. leads to overpopulation, .. causes lower wages, higher land rents, and falling per capital incomes. At first, low wages and high rents bring unparalleled wealth to the upper class, but as their numbers and appetites grow, they also begin to suffer from falling incomes. Declining standards of life breed discontent and strife. The elites turn to the state for employment and additional income and drive up its expenditures at the same time that the tax revenue declines. .. When the state’s finances collapse, it loses the control of the army and police. Freed from all restraints, strife among the elites escalates into civili war, while the discontent among the poor explodes into popular rebellions.

The collapse of order brings .. famine, war, pestilence, and death. .. Population declines and wages increase, while rents decline. .. Fortunes of the upper classes hit bottom. .. Civil wars thin the ranks of the elites. .. Intra-elite competition subsides, allowing the restoration of order. Stability and internal peace bring prosperity, and another cycle begins. (pp.5-8 W&P&W)

Turchin (& coauthor Nefedov) collect much data to show that this is a robust farmer-era pattern, even if there are many deviations. For example, in Europe, 33 of 43 frontier situations gave rise to big empires, yet only 4 of 57 of non-frontier situations did (p.84 HD). “Secular cycles” vary in duration from one to four centuries; Western Europe saw 8 cycles in 22 centuries, while China saw 8 cycles in 21 centuries (p.306,311 SC). During the low instability part of each cycle, instability shows a rough “alternating generations” 50 year cycle of conflict.

I’ll grant that Turchin seems to have documented a reasonably broad pattern, containing most of his claimed elements. Yes, empires tend to start from frontier groups with high cohesion, and core cohesion changes slowly. First there’s war success and a growing area and population, and bigger cities. Eventually can come crowding and falling wages. Inequality also grows, with more richer elites, and this is quite robust, continuing even after wages fall.

While the amount of external war doesn’t change over the cycle, success in war falls. Many signs of social cohesion decline, and eventually there’s more elite infighting, with crime, duels, misspending state revenue, mistreatment of subordinates, and eventually civil war. Big wars can cut population, and also elite numbers and wealth. Eventually war abates and cohesion rises, though not to as high as when the empire started. A new cycle may begin; empires go through 1-3 cycles before being displaced by another empire.

Just as science fiction is often (usually?) an allegory about issues today, I suspect that historians who blame a particular fault for the fall of the Roman Empire tend to pick faults that they also want to warn against in their own era. Similarly, my main complain about Turchin is that he attributes falling cohesion mainly to increased inequality – an “overproduction” of elites who face “increased competition”. Yes, inequality is much talked about among elites today, but the (less-forager-like) ancients were less focused on it.

As Scheidel said in The Great Leveler, inequality doesn’t seem to cause civil wars, and civil wars tend to increase inequality during and after the war (p.203). External wars reduce inequality for losers and increase it for winners, without changing it much overall. It is only big mass mobilization wars of the 1900s that seem to clearly cause big falls in inequality.

In biology, over multiple generations organisms slowly accumulate genetic mutations, which reduce their fitness. But this degradation is countered by the fact that nature and mates select for better organisms, which have fewer mutations. Similarly, it seems to me that the most straightforward account of the secular cycle is to say since empire founders are selected out of a strong competition for very high cohesion, we should expect cohesion to “regress to the mean” as an empire evolves.

That is, in order to predict most of the observed elite misdeeds later in the secular cycle, all we need to assume is a random walk in cohesion that tends to fall back to typical levels. Yes, we might want to include other effects in our model. For example, civil war may allow a bit more selection for subgroups with more cohesion, and humans may have a psychological inclination to cohere more during and after a big war. But mostly we should just expect cohesion to decline from its initial extreme value, and that’s all a simple model needs.

Yes, Turchin claims that we know more about what causes cohesion declines. But while he goes to great effort to show that the data fit his story on which events happen in what order during cycles, I didn’t see him offering evidence to support his claim that inequality causes less cohesion. He just repeatedly gives examples where inequality happened, and then instability happened, as if that proves that the one caused the other.

We already have good reasons to expect new empires to start with a small area, population, and inequality. And this by itself is enough to predict growing population, which eventually crowds to cut wages, and increasing inequality, which should happen consistently in a very wide range of situations. I don’t see a need for, or data support for, the additional hypothesis that inequality cuts cohesion. We may of course discover more things that influence cohesion, and if so we can add them to our basic secular cycle model. But we don’t need such additions to predict most of the cycle features that Turchin describes.

In his latest book, Turchin points out many U.S. signs today of rising inequality and declining social cohesion, and at the end asks “Will we be capable of taking collective action to avoid the worst of the impending democratic -structural crisis? I hope so.” But I worry that his focus on inequality leads people to think they need to fight harder to cut inequality. In contrast, what we mostly need is just to fight less. The main way that inequality threatens to destroy us is that we are tempted to fight over it. Instead, let us try more to see ourselves as an “us” contrasted with a “them”, an us that needs to stick together, in part via chilling and compromising, especially regarding divisive topics like inequality.

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Both Plague & War Cut Capital Share?

I just finished reading Walter Scheidel’s The Great Leveler: Violence and the History of Inequality from the Stone Age to the Twenty-First Century, and found myself agreeing with Scheidel against his critics. Scheidel is a historian who says that inequality has mainly risen in history when income increased, making more inequality physically possible, and when scale and complexity increased, creating more and bigger chokepoints (e.g., CEO, king) whose controllers can demand more rents.

Big falls in inequality have mainly come from big collapses, such as big wars, revolutions, plagues, and state collapses, which are usually associated with violence. This suggests that a big inequality fall is unlikely anytime soon, and we shouldn’t wish for it, as it would likely come from vast destruction and violence. All of which I find very plausible.

While usually big wars via mass mobilization didn’t change inequality much, in the mid 1900s such wars seemed to have gone along with a big taste for redistribution and revolution. This happened to a lesser extent in Ancient Greece and Rome, and fits a story wherein more forager-like cultures care more about redistribution, especially when primed by visible mass sacrifice.

I noticed one puzzling pattern, however. Income in the world goes to owners of capital, to owners of labor, and to those who can take without contributing to production. As the rich usually get more of their income from capital, compared to labor, one thing that can cause less inequality is a change that makes capital earn a smaller share of total income. The puzzling pattern I noticed is that even though big plagues and big wars should have opposite affects on the capital share, both of them seem to have cut inequality, and both apparently in part via cutting the capital share of income! Let me explain.

Big plagues cut the number of workers without doing much to capital, while big wars like WWI & WWII destroy a much larger fraction of capital than they do of labor. Which event, big plague or big war, reduces the share that capital earns? The answer depends on whether capital and labor are complements or substitutes. If they are substitutes, then destroying capital should cut the capital share of income. But when they are complements, it is destroying labor that should cut the capital share.

The simple middle position between complements and substitutes is the power law (a.k.a. “Cobb-Douglas”) production function, where output Y = La*K1-a, for Labor L, capital K, and constant a in (0,1). (Partial derivatives set wages w = dY/dL and capital rent r = dY/dK.) In this situation, the capital share of income r*K/(r*K+w*L) = 1-a, and so never changes.

If, for example, labor L falls by a factor of 2, while capital K stays the same, then wages rise by the factor 21-a while rents fall by the factor 2a, with the product of these factors being 2. Compared to this simple middle position, if labor and capital are instead complements, then in this example wages would rise and rents would fall by larger factors. If labor and capital are instead substitutes, the factors would be smaller.

Economic papers based on data over the last century usually find labor and capital to be complements, though there are notable exceptions such as Thomas Pietty’s blockbuster book. That fits with data on the Black Death. In the century from 1330 to 1430, Europe’s population fell roughly in half, wages doubled, and rents fell a lot. In England, wages tripled. Similar behavior is seen in other large ancient plagues – wages rose by a factor of four in Mexico! This looks more like what you’d see with complementarity than with a simple power law.

World War I (WWI) killed about 1% of the world population, while the concurrent 1918 flu killed about 4%. World War II (WWII) killed about 3%. But capital was cut much more. The ratio of private wealth to national income fell by a factor of two world wide, and by even larger factors in the main warring nations (source):
WealthToIncomeNow for the puzzle. If capital and labor were still complements during WWI & WWII, then destroying a lot more capital than labor should have resulted in rents on capital rising by a factor so big that product of the two factors increases the capital share of income. Is that what happened? Consider Japan, where 5% of the population died:

Real [Japanese] farm rents fell by four-fifths between 1941 and 1945, and from 4.4% of national income in the mid 1930s to 0.3% in 1946. .. By September 1945, a quarter of the country’s physical capital stock had been wiped out. Japan lost 80% of its merchant ships, 25% of all buildings, 21% of household furnishings and personal effects, 34% of factory equipment, and 24% of finished products. The number of factories in operations and the size of the workforce they employed nearly halved during the final year of the war. p.121

Gains from capital almost disappeared during the war years: the share of rent and interest income in total national income fell from a sixth in the mid-1930s to only 3% in 1946. In 1938, dividends, interest, and rental income together had accounted for about a third of the income of the top 1%, with the remainder divided between business and employment income. By 1945, the share of capital income had dropped to less than an eighth and that of wages to a tenth; business income was the only significant revenue source left to the (formerly) wealthy. p.122

In 1946, real GNP was 45% lower than it had been in 1937. p.124

The sharp drop in top income shares .. were caused above all by a decline in the return on capital. .. Most of these changes occurred during the war itself. p.128

Consider also France and Germany (which lost 2% & 11% of people in WWII, respectively):

During WWI, .. a third of the French capital stock was destroyed, the share of capital income in national household income fell by a third, and GDP contracted by the same proportion. ..In WWII, .. two-thirds of the capital stock was wiped out. .. real rents fell by 90% between 1913 and 1950. p.147

[German] rentiers lost the most: their share of national income plummeted from 15% to 3% even as entrepreneurs were able to maintain their share .. real national income was a quarter to a third lower in 1923 than it had been in 1913. p.152

Maybe I’m missing something, but I don’t see how this is remotely consistent with labor and capital being complements. Yet complementarity seems a good fit to big ancient plagues and more recent empirical studies. What gives?

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Alas, Unequal Love

We each feel a deep strong need to love others, and to be loved by others. (Self-love doesn’t satisfy these needs.) You might think we could pair up and all be very satisfied. But this doesn’t happen for two main reasons:

  1. We each prefer to love the popular, whom more others also love. So a few get lots of love, while the rest get less.
  2. We can more easily love imaginary fictional people than real people. Especially ones that more others love.

So even if you are my best source for getting love, the love I get from you may be far less than the love you are giving out, or than I’m giving out. And a few exceptional people (many of them imaginary) get far more love than most people need or can enjoy.

This seems an essential tragedy of the human condition. You might claim that love isn’t a limited resource, that the more people each of us love, the more love we each have to give out. So there is no conflict between loving popular and imaginary people and loving the rest of us. But while this might be true at some low scales of how many people we love, at the actual scales of love this just doesn’t seem right to me. Love instead seems scarce at the margin.

Can we do anything about this problem? Well one obvious fact is that we don’t love people we’ve never heard of. And we can control many things about who we hear of. So we could in principle arrange who we hear about, in order to get love spread out more evenly. But we don’t do this, nor do we seem much inclined to do anything like this. We instead all devote a great deal of time and effort to hearing about as many popular and fictional people as possible. And to trying to be as popular as we can.

I don’t have great ideas for how to solve this. But I am convinced it is one of our essential problems, and it is far from obvious that we’ve given it all the careful thought we might. Please, someone thoughtful and clever, figure out how we might all be much loved.

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Cities As Harems

Many animal species are organized into harems, wherein a single male dominates a group of females and their children. When males become adult, they must leave home and wander singly or in small male groups hoping to tempt harem females into liaisons or to start new harems.

I’ve heard that polygamous sects are often run this way today, with older men kicking out young men when they come of age. But re-reading Montaillou on rural 1300 France makes me realize that humanity has long has related harem-like gender patterns.

Back in 1300 France, centrality gave status. The biggest cities were at the top, above towns and then villages. At the bottom were woodcutters and shepards, all male, who spent most of their time wandering far from villages or towns. Along with soldiers and sailors, these men lived dangerous low-status high-mobility lives in sparse areas. They sometimes tempted women into liaisons, or made it rich enough to start a family in a village. Such mating strategies may explain why such men moved so often even they were poor and moving is expensive.

Back in the high status centers, there remained a few high status men and women, many low status women, but fewer low status men. The lower status women were often servants to high status males, and often had affairs with them.

In the US today, the states with the most men relative to women are Alaska, Wyoming, North Dakota, Nevada, Utah, and Montana — mostly harsher low density areas. In contrast, the states with the most women relative to men are District of Columbia, Rhode Island, Maryland, Massachusetts, New York, near some of our biggest high status cities. Most big US cities have more women than men. The exceptions are San Jose, San Francisco, Las Vegas, Honolulu, Austin, Seattle, San Diego, places with new booming, mostly tech, industries. Men are more willing to move to try new often-harsher industries and places.

We hear college-educated women complain today that there aren’t enough college-educated men to go around, either during college itself or afterward. Of course there are plenty of other men around, but these women mostly consider such men beneath them. Seems to me this isn’t that different from 1300 France; women are more eager to locate near high status people. They focus on high status men, and lament there aren’t enough to go around.

Sometimes people fear today that low status men unhappy from being unable to find women will cause havoc. But in the past men avoided such feelings successfully by just avoiding women. By rarely seeing women they less often felt the envy that might cause havoc. If there’s a bigger problem today it might be because low status men more often come into contact with attractive but unavailable women. From this perspective, maybe low status men avoiding women via male-oriented video games isn’t such a bad thing?

Added 5July: Birds are like this too.

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Hive Mind

Some people like murder mystery novels. I much prefer intellectual mysteries like that in Garett Jones’ new book Hive Mind: How Your Nation’s IQ Matters So Much More Than Your Own:

Over a decade ago I began my research into how IQ matters for nations. I soon found that the strong link between average IQ and national productivity couldn’t be explained with just the conventional finding that IQ predicts higher wages. IQ apparently mattered far more for nations than for individuals. In my early work, I estimated that IQ mattered about six times more for nations than for individuals: your nation’s IQ mattered so much more than your own. That puzzle, that paradox of IQ, is what set me on my intellectual journey. …

I’ll lay out five major channels for how IQ can pay off more for nations than for you as an individual: Continue reading "Hive Mind" »

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