Why money matters

I think this is an absolutely fantastic, clear and concise explanation for why money matters in macroeconomics:

In normal times, people receive apples and money from the sky in the form of endowments (i.e. their wealth), and they make decisions about how to balance their cash and apple balances. Apples are transacted, bellies are filled, and life is good.
But suddenly, a recession hits. What does this look like? By definition, a recession is when there is a general glut of goods that aren’t consumed. In this toy economy, this corresponds to a situation in which some people have apples but choose not to eat them! This may seem peculiar, but remember that the market for apples in this model represents a composite of all goods markets. So it could be the case that while everybody has apples, some want Red Delicious while others are looking for the tartness of Granny Smith. In more formal economic models, this is glibly incorporated by requiring that people do not consume their own endowment and instead trade for consumption. In any case, apples aren’t eaten and we have a rotten general glut.

But this seems peculiar — aren’t markets supposed to clear? Not necessarily. Prices don’t always adjust instantly, so we can have excess supplies and excess demands. However, economists do have a way to constrain what this non-clearing state looks like. In particular, according to Walras’ law, assuming everybody spends all of their wealth, if there are excess supplies (i.e. too much produced) in some markets, then they must add up to excess demands (i.e. too little produced) in other markets. In other words, even if supply does not equal demand in each market, supplies must add up to demands across markets.

The requirement that everybody spends their endowment is crucial. It means that Walras’ law doesn’t apply just to the market for apples because not everybody spends all their wealth on apples. Instead, some people may put their wealth in money. But once we include the money market, we do have the condition that everybody spends their endowment, and therefore Walras’ law does apply to the entire macroeconomy of apples and money.

This leads to the most important conclusion from general equilibrium theory as related to monetary economics:

If there is an excess supply of goods, it must be the result of excess demand for money.

Go read the whole thing and follow Yichuan Wang’s blog. He’s one of the best communicators in economics right now.

Broken heartland

Every so often someone writes about how this or that region is going to collapse. Today we get a perspective on why agriculture will collapse in the Great Plains:

The seed, it turned out, was organic, as were all of Teske’s crops — though he was quick to clarify that this was “not just for moral reasons.” On the contrary, organic farming seemed to him the only sensible option left. Decades of innovation had turned conventional farming into such an expensive and technical proposition that it was hopeless for anyone but agribusiness conglomerates to attempt it. This, he said, was the real cause of depopulation. Modern technology made it possible, and more or less obligatory, for a single owner to work fifty times as much land. So neighbors got to buying out neighbors, and then were bought out themselves. The only way forward, Teske figured, was to reject all those modern innovations, at which point you were basically “organic.”…

Sprawling beneath eight states and more than 100 million acres, the Ogallala Aquifer is the kind of hydrological behemoth that lends itself to rhapsody and hubris. Ancient, epic, apparently endless, it is the largest subterranean water supply in the country, with an estimated capacity of a million-billion gallons, providing nearly a third of all American groundwater irrigation. If the aquifer were somehow raised to the surface, it would cover a larger area than any freshwater lake on Earth — by a factor of five.

Within a decade thousands of wells were drilled, creating a spike in productivity as unprecedented as it was unsustainable. Land that had been marginal became dependable; land that was dependable became bountiful. Even as the U.S. population surged, with soldiers returning and babies booming, the output of the plains rose fast enough to meet and exceed demand…Then, during the early 1990s, farmers throughout the Great Plains began to notice a decline in their wells. Irrigation systems from the Dakotas to Texas dipped, and, in some places, have been abandoned entirely…None of which, he went on, is likely to come back. For complex reasons involving wind, weather, and soil composition, the Ogallala does not recharge in the way one might expect. In fact, of the eight states above the aquifer, only Nebraska, with its sandhill dunes, is permeable enough to contribute any serious replenishment…

The farmers we stopped to talk with seemed to break his heart more each day. On a 12,000-acre plantation near Weskan, Kansas, we stood inside a cavernous warehouse of gleaming tractors and combines while the owner chattered and Teske interjected questions about loan terms and well output. He nodded gravely at the answers and chomped on the stub of his cigar until, as we headed down the driveway, his face collapsed and he moaned, “That poor bastard can’t even see the cliff he’s going off.”

One of the major ecological problems in the country – in the world – is the drainage of aquifers and dwindling water supply. It actually shocks me how little news it gets. Read the article, it’s absolutely great writing and had me gripped throughout the long, long article.

Photo from

In which Harvard Business School is revealed to be an episode of Gossip Girl

My biggest takeaway from this in-depth piece on gender equality in Harvard Business School is that it sounds like Gossip Girl is real, it’s just in Cambridge:

But she wanted to meet someone soon, maybe at Harvard, which she and other students feared could be their “last chance among cream-of-the-crop-type people,” as she put it. Like other students, she had quickly discerned that her classmates tended to look at their social lives in market terms, implicitly ranking one another. And like others, she slipped into economic jargon to describe their status.

The men at the top of the heap worked in finance, drove luxury cars and advertised lavish weekend getaways on Instagram, many students observed in interviews. Some belonged to the so-called Section X, an on-again-off-again secret society of ultrawealthy, mostly male, mostly international students known for decadent parties and travel.

Update: From the reaction to the original article, the New York Times is astonished to learn that perhaps class is an issue, too, at HBS:

When Christina Wallace, now the director of the Startup Institute, attended Harvard Business School on a scholarship, she was told by her classmates that she needed to spend more money to fully participate, and that “the difference between a good experience and a great experience is only $20,000.” [emphasis added]

…The result is a school that mixes students of relatively modest means with extremely wealthy ones, including in recent years the children of Leon Black, a private equity investor, and Gerald D. Hines, the founder of one of the world’s largest real estate firms, among many others….

According to Ms. Boyarsky and others, the members are mostly male and mostly international students from South America, the Middle East and Asia. They organize “the real parties, the parties where it’s a really limited list, the extravagant vacations — I mean really extravagant,” she said. (No students interviewed admitted to being members of the group, though some said they had attended its parties.)

“More than once I heard that ‘the only middle-class students here are the Americans,’” another recent graduate said. [Which is clearly not true when read after the preceding paragraph.]

Real info on HFT

There is a great comment from a higher-frequency trader that was left on a post on Marginal Revolution. It’s a few comments down on that page. Here’s the background that he gives:

I work as a quant at one of the major high frequency trading firms, this paper is definitely one of the better academic works I’ve seen on the subject. I’ll add a little more. Generally the way that HFT works is by looking for a set of predictive signals in the market. Those signals are combined with liquidity and execution constraints to try to find the most profitable set of parameters after transactions costs are taken into account.

90% of these signals are fall into two major categories: 1) Looking at the price movements of related securities. A good example is SP500 versus Nasdaq. The correlation between the two is around 85% on a daily horizon, but over a horizon of 10 secs or so correlation is virtually zero. So when one moves a certain you bet that the other one will either follow or the first mover will fall back. 2) The other one is by looking at the state of the limit order book and it’s evolution through time. As a very simple example if say you have 20,000 size quantity on the bid and it’s been monotonically increasing and 5,000 size quantity on the ask and it’s monotonically decreasing then it’s very likely that the level on the ask will get wiped out first and the price will go up.

In general what these two add up to is trying to distinguish noisy trades versus signal trades. Speculators/investors/hedgers/etc. are the primary players in the market. Some of those trades contain high information (e.g. maybe a person with access to insider information buying up stock before some announcement), some of them contain virtually no information and are pure noise (e.g. granny liquidating some of her portfolio for monthly expenses). In a naive market with no HFT signals we have no way of assessing the informational content of individual trades, we only have an estimated aggregate or average informational content of trade. Market makers will set their spread and sizes according to this aggregated informational content.

But over any sample the estimated average informational content of trades will not be the same as the realized, for example one week might more than usual insider trading, one month it might make up a small fraction. There’s also a ton of path dependency when you work out the math, that amounts to pure randomness. Because of this securities will not perfectly track their “true price.” The deviation is still stationary, because the more out of line the prices get with the fundamentals the more speculators will step in and push it back. No one is smarter than the market 100% of the time so every time a fundamental speculator sees a price that’s too low/high there’s some chance that the market is right and his valuation is missing something and some chance he’s right. Speculators that aren’t very good are probably only going to be “beating the market” when the valuation on securities looks insanely out of whack or by distributing his portfolio over a wide range of perceived mis-valuations to reduce his volatility. Only the very best speculators are going to be able to get their fundamental valuations consistently right within a small margin of error. So without HFT/Stat Arb./technical trading/whatever you want to call it/etc. the thing that keeps securities from randomly drifting too far are fundamental speculators.

Basically what HFT is doing, instead of fundamentally valuing securites, determining the informational content of individual trades or small time frames, using the signals I mentioned earlier. A segment of the price evolution with high information content tend to look very different from noisy trades on the small scale, but when aggregated up lose this distinguishability. It’s almost symmetrical when you think about it. Fundamental speculators estimate a price for the security and trust in the reliability of the price evolution process in brining the market price to their estimated “true price”. HFT trusts in the reliability of the initial price as being the best estimate of the value of the security and tries to identify errors and miscalculations in the price evolution process.

There is a lot more to that comment there, and plenty of other worthwhile comments as well. Go give them a read!

Manufacturing: Both down and up

Everyone loves to talk about the decline of American manufacturing. Here is manufacturing employment as a percent of the workforce:

It increased through WWII, then held steady for about ten years, then has been decreasing linearly since then (from which we can confidently predict that we will have no one working in the sector in 25 years). Let’s look at total manufacturing employment, though:

Here we see the total number of jobs increasing until about 1970-1980, then plateauing until the 2000’s when it dropped precipitously. I think this tells a very different story than the previous graph. When we examine manufacturing employment and total output we see that output has been increasing consistently:

Which obviously brings us to the productivity gains:

I don’t have any particular commentary on this, I just think it’s good to keep all the facts in mind. Note, though, that productivity increases do not really lead to job loss, but they might lead to decreased share of employment (see also: agriculture). The comments in the links are occasionally insightful, so go ahead and browse through them.

Efficient Presidents Hypothesis

Isn’t that a great chart? And since the shorter US Presidential candidate has won the popular only 33% of the time, shouldn’t the EMH suggest that intrade betting converges months ahead of time? Arbitrage should be easy. I wonder how the two variables interact?

Pre-fabricated post-apocalyptic cities

We keep hearing about pre-fabricated cities designed for millions of people popping up all over China. With the hundreds of millions of people arriving into cities from the countryside, this might be a simple solution to an overcrowding problem. Besides the clear impression that China hasn’t learned much from New Urbanism, there’s also the problem of these planned cities failing. China is trying to plan these things en masse, and not letting them grow organically. Perhaps twenty years down the line they will be a useful, though crumbling, investment. Until then, there will be the few residents wandering like post-apocalyptic zombies through a landscape of empty skyscrapers and the shells of extravagant museums.

Continue reading

Education in times of cholera

How important is education to an economy when it is trying to take advantage of new technology or economic situation? An analysis at VoxEU looked at Prussian advances during the industrial revolution and found that education levels predicted industrialization levels.

But while the developments in England were promoted by various factors (e.g. on this site Allen 2009 stresses the role of low energy prices and high wages) or might even have been rather accidental, they have been somewhat different on the European continent. When new machines and work processes became available in England, other countries just had to copy them. The adoption of English techniques and machines was therefore crucial to catch up to the technological frontier. But the pre-conditions were not equal for all. Some regions just had too little education for immediate adoption. So far, the literature has given little attention to the role of education in the catch-up of technological follower nations during the Industrial Revolution.

…By 1816, Prussia had become the world leader in primary schooling (see Lindert 2004) with an average per-county enrolment of 58% among the 6 to 14 year old. Nevertheless, there was strong regional variation in enrolment rates. The county with the lowest enrolment only sent 3% of its children to school in 1816, while it was 95% in the county on the other end of the distribution (Figure 1). In 1849, Prussian enrolment rates had reached an average of 80%. Subsequently, in 1871 we observe an average literacy of 84% in the adult population.

…The aggregate result of the importance of education for industrialisation conceals important sector differences, though. It turns out that in the textile industry, where innovation was less disruptive and child labour more prevalent, education had no effect in either phase of industrialisation. The literature often confirms that the development in textiles was different, because of path dependence, high sunk costs, and child labour. Industrial development in the non-textile sectors, which experienced more radical change or even evolved all new, depended on the availability of an educated population that was earlier aware of the productive potential of new technologies and more capable of adjusting to changed situations.

Basically, education was required for society to easily copy and understand new technologies. Beyond the obvious implications for emerging and competing economies today is its importance to displaced workers. Here is a chart of productivity performance in Latin America:

The important time periods are 1950-1975 (the important-substitution years) and 1990-2005 (the Washington consensus years). The link suggests that the worse overall performance during the Washington consensus years is almost entirely due to a lack in desirable structural change.

For all its faults, IS promoted rapid structural change. Labor moved from agriculture to industry, and within industry from lower-productivity activities to higher-productivity ones. So much for the inherent inefficiency of IS policies!

Under WC, firms and industries were able to accomplish a comparable rate of productivity growth, but they did so by shedding (rather than hiring) labor. The displaced labor went not to higher-productivity activities, but to less productive lines of work such as informality and various services. In other words, the WC ended up promoting the wrong kind of structural change.

This account reinforces the centrality of structural change in driving rapid economic growth. It should also cause us to be wary of productivity studies that focus on what is happening within manufacturing alone. After all, productivity within manufacturing can be stellar, but if manufacturing or other high productivity sectors as a whole are rapidly shedding labor, economy-wide productivity performance will be disappointing.

So what’s happening during our economic challenges today? Are previously productive factory workers moving to other, more productive jobs? Doesn’t seem like it. Factory owners are even starting to increase the workers they hire, but they’re having trouble finding workers with the needed skills – basic literacy and ninth grade math! Again it’s clear that our most important investment is our education system. So why are our public schools losing their funding and going down the gutter.

Here’s a related article on the American jobs machine. The key quote:

Resources – including labor – released via higher productivity are supposed to be channeled into expanding sectors. Moreover, productivity growth is supposed to yield improved economic outcomes via higher real wages. Yet as spencer famously shows, labor’s share of output has been steadily decreasing since the early 1980s. This downward trend was interrupted by gains evident during the tech bubble of the mid-1990s. Apparently, only during that brief, shining moment of generational technological change did the productivity story work as we believe it should, at least since the early 1980’s.

Greg Mankiw links to a similar article relating (educational) inequality to the financial crisis. It’s thesis is kind of dumb (and partisan), but the ideas are interesting.

Asia’s miraculous miracle

This essay by Krugman on Asian development was being posted everywhere for a while. Everyone was trying to predict things about China with it, which is of course silly, but it is very useful as a bit of economic history. When writing about pretty much anything, people forget what they had learned in the past and pretend like everything is new. Or they just don’t learn about the past, which is just as bad.

…The leaders of those nations did not share our faith in free markets or unlimited civil liberties. They asserted with increasing self confidence that their system was superior: societies that accepted strong, even authoritarian governments and were willing to limit individual liberties in the interest of the common good, take charge of their economics, and sacrifice short-run consumer interests for the sake of long-run growth would eventually outperform the increasingly chaotic societies of the West. And a growing minority of Western intellectuals agreed.

The gap between Western and Eastern economic performance eventually became a political issue. The Democrats recaptured the White House under the leadership of a young, energetic new president who pledged to “get the country moving again”–a pledge that, to him and his closest advisers, meant accelerating America’s economic growth to meet the Eastern challenge.

The time, of course, was the early 1960s. The dynamic young president was John F. Kennedy. The technological feats that so alarmed the West were the launch of Sputnik and the early Soviet lead in space. And the rapidly growing Eastern economies were those of the Soviet Union and its satellite nations.

…We all do a primitive form of growth accounting every time we talk about labor productivity; in so doing we are implicitly distinguishing between the part of overall national growth due to the growth in the supply of labor and the part due to an increase in the value of goods produced by the average worker. Increases in labor productivity, however, are not always caused by the increased efficiency of workers. Labor is only one of a number of inputs; workers may produce more, not because they are better managed or have more technological knowledge, but simply because they have better machinery. A man with a bulldozer can dig a ditch faster than one with only a shovel, but he is not more efficient; he just has more capital to work with. The aim of growth accounting is to produce an index that combines all measurable inputs and to measure the rate of growth of national income relative to that index–to estimate what is known as “total factor productivity.”

…When economists began to study the growth of the Soviet economy, they did so using the tools of growth accounting. Of course, Soviet data posed some problems. Not only was it hard to piece together usable estimates of output and input (Raymond Powell, a Yale professor, wrote that the job “in may ways resembled an archaeological dig”), but there were philosophical difficulties as well. In a socialist economy one could hardly measure capital input using market returns, so researchers were forced to impute returns based on those in market economies at similar levels of development. Still, when efforts began, researchers were pretty sure about what they would find. Just as capitalist growth had been based on growth in both inputs and efficiency, with efficiency the main source of rising per capita income, they expected to find that rapid Soviet growth reflected both rapid input growth and rapid growth in efficiency.

But what they actually found was that Soviet growth was based on rapid growth inputs–end of story. The rate of efficiency growth was not only unspectacular, it was well below the rates achieved in Western economies. Indeed, by some estimates, it was virtually nonexistent.

It goes on to discuss Singapore, Japan, and China. It is shocking to realize how much improvements in accounting were important to economic analysis. One note to this article: Singapore now has a higher GDP per capita than the USA in PPP terms but not nominal terms.

Humans are a collective intelligence

People like to wonder – why are humans the dominant life form on the planet? Why are we special? Even though humans are probably not as special as we like to think, there is one thing that seems to separate us from other animals: culture. Culture, trade, speech, sociality. These seem to be the ingredients for human dominance and ‘intelligence’. We are better at some cognitive tasks, but was that enough? Did we get smart or just get together? I tend to think the answer is that we got together:

Scientists have so far been looking for the answer to this riddle in the wrong place: inside human heads. Most have been expecting to find a sort of neural or genetic breakthrough that sparked a “big bang of human consciousness,” an auspicious mutation so that people could speak, think or plan better, setting the human race on the path to continuous and exponential innovation.

But the sophistication of the modern world lies not in individual intelligence or imagination. It is a collective enterprise. Nobody—literally nobody—knows how to make the pencil on my desk (as the economist Leonard Read once pointed out), let alone the computer on which I am writing. The knowledge of how to design, mine, fell, extract, synthesize, combine, manufacture and market these things is fragmented among thousands, sometimes millions of heads. Once human progress started, it was no longer limited by the size of human brains. Intelligence became collective and cumulative.

…Agriculture was invented where people were already living in dense trading societies. The oldest farming settlements of all in what is now Syria and Jordan are situated at oases where trade routes crossed, as proved by finds of obsidian (volcanic glass) tools from Cappadocia. When farmers first colonized Greek islands 9,000 years ago they relied on imported tools and exported produce from the very start. Trade came before—and stimulated—farming.

It is precisely the same in cultural evolution. Trade is to culture as sex is to biology. Exchange makes cultural change collective and cumulative. It becomes possible to draw upon inventions made throughout society, not just in your neighborhood. The rate of cultural and economic progress depends on the rate at which ideas are having sex.

…This theory neatly explains why some parts of the world lagged behind in their rate of cultural evolution after the Upper Paleolithic takeoff. Australia, though it was colonized by modern people 20,000 years earlier than most of Europe, saw comparatively slow change in technology and never experienced the transition to farming. This might have been because its dry and erratic climate never allowed hunter-gatherers to reach high enough densities of interaction to indulge in more than a little specialization.

Where population falls or is fragmented, cultural evolution may actually regress. A telling example comes from Tasmania, where people who had been making bone tools, clothing and fishing equipment for 25,000 years gradually gave these up after being isolated by rising sea levels 10,000 years ago. Joe Henrich of the University of British Columbia argues that the population of 4,000 Tasmanians on the island constituted too small a collective brain to sustain, let alone improve, the existing technology.

There is some theoretical support for the get-together hypothesis. The authors extend a previous model of cultural transmission to include a more realistic structured metapopulation, among other things. In their model, each individual attempts to learn from the most-skilled individual, but an imperfect learning process leads to an average net loss in skill. However, individual errors (“inaccurate inferences”) occasionally allow some learners to acquire an even greater skill during transmission. The world is divided into subpopulations that are connected by a Gaussian random-walk migratory activity.

Whereas the earlier model suggested that population size was the primary variable that fixed the average cultural skill, the metapopulation analysis indicated that this was only true for small numbers of subgroups (~<50). For larger numbers of subgroups (states, tribes, etc.) the skill accumulation depends on the degree of interaction between subgroups. In fact, the benefits of migration/trade were highest for cultural skills with the most complexity. Perhaps we should be thankful we outbred the neanderthals.

[Photo from]

Update: And of course a paper comes out on just this hypothesis! They show how population size predicts technological complexity in Oceania. When population drops, technology drops. Here is a good post about the paper, and here is another.

Modern macroeconomics finally rears its head

Via Greg Mankiw, here’s a good article on the state of modern macroeconomic modeling by the frequently-interesting Kocherlakota. There is a lot of nuance and detail in here that is missing from the conversations one usually hears about modern macroeconomics. I think it would be worthwhile to go through some of the references at some point, too.

The switch to modern macro models led to a fierce controversy within the field in the 1980s. Users of the new models (called “freshwater” economists because their universities were located on lakes and rivers) brought a new methodology. But they also had a surprising substantive finding to offer. They argued that a large fraction of aggregate fluctuations could be understood as an efficient response to shocks that affected the entire economy. As such, most, if not all, government stabilization policy was inefficient….Scholars in the opposing (“saltwater”) camp argued that in a large economy like the United States, it is implausible for the fluctuations in the efficient level of aggregate output to be as large as the fluctuations in the observed level of output. They pointed especially to downturns like the Great Depression as being obvious counterexamples.

…With the advent of better computers, better theory, and better programming, it is possible to solve a much wider class of modern macro models. As a result, the freshwater-saltwater divide has disappeared. Both camps have won (and I guess lost). On the one hand, the freshwater camp won in terms of its modeling methodology. Substantively, too, there is a general recognition that some nontrivial fraction of aggregate fluctuations is actually efficient in nature. On the other hand, the saltwater camp has also won, because it is generally agreed that some forms of stabilization policy are useful. As I will show, though, these stabilization policies take a different form from that implied by the older models (from the 1960s and 1970s).

…However, the models with asset market frictions (combined with the right kind of measurement from microeconomic data) make clear why the above analysis [of frictionless financial markets] is incomplete. During downturns, the loss of income is not spread evenly across all households, because some people lose their jobs and others don’t. Because of financial market frictions, the insurance against these outcomes is far from perfect (despite the presence of government-provided unemployment insurance). As a result, the fall in GDP from June 2008 to June 2009 does not represent a 4 percent loss of income for everyone. Instead, the aggregate downturn confronts many people with a disturbing game of chance that offers them some probability of losing an enormous amount of income (as much as 50 percent or more). It is this extra risk that makes aggregate downturns so troubling to people, not the average loss.

This way of thinking about recessions changes one’s views about the appropriate policy responses. Good social insurance (like extended unemployment benefits) becomes essential. Using GDP growth rates as a way to measure recession or recovery seems strained. Instead, unemployment rates become a useful (albeit imperfect) way to measure the concentration of aggregate shocks.

[photo from]

Lists to learn about life

A brief post. I never realized economic history was a thing until I started listening to Brad DeLong’s lectures on the American Economic History. I would love to learn more about the subject so any book recommendations would be welcome. Here are some books that I’m going to read to start myself off on international economic history. The first few (besides Guns, Germs, and Steel):

The Auteurs has also compiled a list of Criterion Collection titles on Netflix’s Watch Instantly feature. It’s handy to have a quick index of good movies for times of need. Some titles to tantalize:

  • Solaris
  • The Royal Tenenbaums
  • Lord of the Flies
  • Rashomon
  • The Last Temptation of Christ
  • Revanche

The more things change, the more they will change

Well it’s that time of the year again – time to predict the decline of America. The two currently making the rounds are in The Atlantic and Foreign Policy. Everyone’s worrying about how China’s going to eclipse the USA soon, and the other members of BIC aren’t far behind. Of course, what should really matter is GDP per capita, where we’re realistically doing pretty darn well – though I still think these statistics are misleading (maybe a better calculation is here?). But in terms of world power, we’re unerringly headed down a much more multipolar path – especially if Europe can ever get its act together.

But as the Atlantic article points out, in a lot of ways we’re already doing pretty poorly. Take infrastructure, for instance; driving in San Diego often feels like you’re in a warzone with all the potholes, and the rest of California isn’t doing much better. Of course, part of this has to do with the time they were built. Go look at China’s shiny new highways in sixty years! But that’s also the problem – our infrastructure is old, and we’re not doing enough about it. And everything we need to do – improve education, stop discouraging immigration, improve our transit system – is being held back by conservative fears. Sometimes people have a hard time realizing they’re not as great as they think they are.

But nothing ever stays the same; everyone likes to talk about how people said the same things about Japan twenty years ago, and look how that turned out. Of course, they’re assuming that we’re not going to be the ones making the mistake this time, but on the broader point they’re right. The future is never how you predict it will be. There’s one thing that will remake the world in ways that we can’t imagine, and no one’s thinking about the consequences of it: artificial intelligence.

No, I’m not talking about the talking robot, super-intelligent type of AI. I’m talking about the kind we have now. The boring kind. The kind that can beat grandmasters at chess, clean your carpets, and find answers to your questions. How many people are really aware of how large of a paradigm shift this will be? What happens when we can automate intelligent work? Plenty of financial services are already done by computer – and can dominate slower, human-based stock investing techniques. There’s plenty of chatter about news aggregators that can read clips of information on the internet and actually write news stories – so long news journalists. People will still clearly be employed in the future, my point is that things are going to change so dramatically, how can we begin to predict what will be happening in the world in the next fifty years?

[Photo from here]

Economics of the past are no indication of the future

I like population numbers. I like maps. I like empires. I like history. Here’s a list of the largest empires throughout history, with all sorts of fancy stats. Things you’ll learn:

  • In the fifth century BC, the Persian empire had 44% of the world’s population
  • In 1870, the British Empire accounted for 35.9% of the world’s GDP, followed closely by America’s 35% in 1945
  • Nazi Germany’s economy was roughly half the size of Britain’s in 1938
  • India in the second century BC had a larger population than China in the 1100s

Also, from a different source I found out that in 1913, Europe was more populated than China and it combined with its former colonies in North America made up 33% of the world’s population.

[photo from]

Mr. Gorbachev was not the one to tear down these walls

This essay about the destruction of old borders in Europe, and the creation of new ones, is both fascinating and captures the feel of the continent spot-on. I hope it becomes a longer essay, there’s rather a lot to say about where Europe’s heading and how that will change the sense of identity for many, many people:

Yet, while attending several celebratory conferences over the last few months about the revolutions of 1989, I found myself thinking about the new walls that have risen within and around Europe. The same Schengen agreements that create a zone of free internal movement for all but a few EU members require the construction and defense of a single external European frontier. This is as it must be; but with the result that someone from beyond the zone, from Belarus or from Turkey, may find herself refused entry. The difference between not needing a passport at all and needing a special visa is considerable, and creates a distance that can be psychologically and politically damaging.

Europe itself seems much further from the United States than it did twenty years ago. In western Europe, the election of Obama has undone some of the feeling of estrangement that resulted from the American decision to invade Iraq; while in some east European countries, such as Poland, Obama sometimes takes the blame for the weakened American position in world affairs. In the meantime European and American society have become increasingly incomprehensible to one another. Standards of living in several west and central European countries are now much better than in America. I did not expect twenty years ago that poor Poland could become so quickly comparable to the United States in its infant mortality rates and life expectancy. Its downtowns feel safer and its public transportation is better. Although Poles and other east Europeans are more likely than West Europeans to repeat the rhetoric of economic libertarianism, they accept the fundamentals of the welfare state that in America are now so contested. It is no easier to explain American debates over health care reform in Warsaw than in Vienna.

Yet all is far from well within Europe. In much of central and eastern Europe, nationalist populism—whether in Russia, Poland, Hungary, or Austria—is more resonant than twenty years ago. Throughout the continent, pedagogical systems have remained national, or, in such cases as Russia and Ukraine, become so. Young people in almost every European school system learn versions of history more appropriate for the nineteenth century than the twenty-first.

[photo from]