By Simon Grey, on December 22nd, 2011
So what are the best bets when buying student debt? Technical schools. Students pay the least for their education with the potential to make good money after graduation in only a couple of years.
By that arithmetic, technical colleges come out on top, Mr. [Daniel] Ades said. “We’re in a skills based economy and what we need is more computer programmers, more [nurses],” he said. “It’s less glamorous but it’s what we need.”
Meanwhile the nation’s law schools continue to over-supply the nation with lawyers. Law students are borrowing an average of $68,827 at state schools and $106,249 a private schools only to add to the glut of barristers.
In what must undoubtedly be a shock to humanities majors, people who are actually know how to do useful things are in position to make money after they graduate. Imagine that.
Of course nurses and programmers are going to be in a position to make good money, mostly because people want to not be sick, maim, or injured, and they also like to be able to use electronic gadgets. Hence the reason why students who graduate from technical schools with degrees in nursing and programming are in a better position to be employed than, say, an English major. As fascinating as Faulkner undoubtedly is, being able to expound upon his work at length is not something many consumers really want to pay for.
As such, it should no surprise that people who learn actual, useful skills in college are in a better to make money than those who majored in something that is considerably less practical.
By Ethan Zuckerman, on October 13th, 2011
The member meeting at the Media Lab features speakers from within the lab, like César Hidalgo and Joi Ito, and outside speakers – in that latter case, the invited speakers reflect César’s wonderfully idiosyncratic take on networks. One of his major collaborators is Ricardo Hausmann, director of Harvard’s Center for International Development and former Minister of Planning for Venezuela.
Hausmann argues that to succeed economically, humans have learned how to specialize. Someone who’s marvelous in one area is likely mediocre at others – consider Michael Jordan’s ill-fated attempts to play professional baseball. Some tasks require a full human’s worth of knowledge – a person-byte – to carry them out successfully. Others require much more knowledge – building a complex product like a computer might require a kilo-person byte or more – the highly specialized knowledge and skills of a thousand different people. “Modern man is useless as an individual. Making a computer is a team sport.”
By understanding how much knowledge and coordination different economies are capable of, we might understand their economic growth potential. In the US, the average employee works with 100 coworkers. In India, the average employee works with 4 coworkers. Hausmann explains that’s not coincidental – the difference in wealth and income between the nations is closely related to the ability of firms to take on complex tasks. This also helps explain recent disappointment with the limited impacts of microlending – those loans go to small firms that are limited in terms of personbytes. They’ve only got so much knowledge they can apply to producing complex and high value products.
We might characterize economies in terms of those where lots of people do very simple work – he illustrates this with a marvelous Edward Burtynsky photo of assembly line workers processing chicken in China – and those where individuals do complex things in consort, like the players within a symphony orchestra. Hausmann shows us a “map” of the world, a complex graph that represents nations and what products they produce. Most nations produce a few things, and a few produce many different things. Some products are made everywhere, while others are made in very few places.
There’s an underlying pattern to this. The nations that make only a few things all tend to make, more or less, the same things. Basically, we can divide the world into two sets of countries – those that have sufficient personbytes of knowledge to produce a wide range of goods, and those that can produce only a few simple things. The places that make everything make things that few others make. Hausmann explains that products require a specific set of personbytes to produce. When you gain additional personbytes of skill, it’s like getting new letters in Scrabble – you can produce a new set of words, but only within the constraints of the letters (skills, knowledge) you already have.
“Poor countries make few things, and things that everyone makes. Rich countries make unique things. And this is true for municipalities as well as for countries.” He shows a graph of manufacturing in Chile that looks curiously like his graph of the world – on the top is Santiago, where people manufacture all sorts of things… on the bottom “is where there’s nothing but penguins” and capacity for manufacturing is very low.
Global economics, Hausmann explains, is a little like the BCS scoring in college football. It’s not just about who you beat, it’s about who they beat as well. What do you make, and what does everyone else make? What do you make that no one else makes? What new products could you manufacture based on what you already make?
Why pay attention to this idea, the “economic complexity index”? It’s a very good tool for explaining the classic question of “Why are some countries rich and others poor?” Specifically, it explains 73% of the variances of incomes across nations. And where the predictions economic complexity theory offers differ from reality, it’s possible that reality is wrong. The index suggests that India should be richer and Greece should be poorer, which suggests that error in the index is predictive of future growth. If you want to bet on economies that are undervalued, Hausmann suggests you invest in China, India, Thailand, Belarus, Moldova and Zimbabwe. (On the last, he suggests that Zimbabwe’s main economic problem is a single persistent individual, but that there are many personbytes of knowledge ready to produce goods once the political situation changes.)
Is economic complexity actually measuring another phenomenon, like education? Probably not. We can look at investment in education and economic growth, and education appears to correlate more weakly than economic complexity. He suggests we look at Ghana, which has invested heavily in education since 1975, and Thailand, which hasn’t invested as heavily. Ghana hasn’t moved far from a largely agricultural economy, while Thaliand has moved from producing jute and sugar to becoming a major manufacturing center. They’ve accumulated many personbytes even if they didn’t invest heavily in education.
This raises a tricky question – how do you become a watchmaker in a country without watchmakers? The answer is that you move from what you currently produce to products that require only a fractional increase in personbytes, from one product space to a closely related one. The question for economic success may be how close you are to good products from what you already know how to make.
I find Professor Hausmann’s theory fascinating, in part because I’ve had the chance to play with the gorgeous visualizations César has built of economic progress in different parts of the world based on economic complexity. What I still don’t understand is how Thailand kicked Ghana’s butt economically. How do you get from jute to microcircuitry? And why couldn’t Ghana get from aluminum production to more complex manufacturing. Looking forward to reading his papers and understanding a bit more, as the core concept of complexity is a very compelling one.
By Greg Beatty, on July 9th, 2008
Infotopia: How Many Minds Produce Knowledge. By Cass R. Sunstein. Oxford University Press, USA, 2008. 304 pages. $15.95.
 If you’re interested in how organizations and societies process knowledge or how what one individual knows diffuses through a larger social matrix, read Cass Sunstein’s Infotopia. It’s not perfect, but it does a fine job of analyzing a range of possibilities for aggregating individual knowledge—and it’s fun.
That Sunstein’s perspective is largely positive is indicated through the title; “infotopia” resonates with “utopia,” and there’s more of a trace of the hope that through collective understanding, we might shape a perfect society. There’s also a nice play on words here. “Utopia” can mean either “eu-topos,” the good place, or “ou-topos,” a place that does not exist, and so allows for ambiguity and hypotheticals. Well, Sunstein describes an ideal society whose place is in information and which is accessed via information and constructed upon information. To learn and discourse is to become a citizen of Infotopia.
Sunstein’s core problem is this: how do we aggregate dispersed knowledge? Or how do we as a group come to know what distinct individuals know? Sunstein follows two interweaving strands of analysis. One examines how groups actually do aggregate information known by individuals; the other strand examines how groups should do so. He pursues both analytical paths through discussing several areas of communal knowledge processing.
He starts with a, yes, utopian vision of what might happen if information aggregation worked everywhere as promised, then moves into discussions of numerous failures. From there, Sunstein spends a chapter each on major approaches to aggregation: statistical combinations, deliberation, markets and via the Internet. Examples are given in each case—often first hand and/or amusing examples—and general principles are derived from them. A historical and theoretical frame is given in each case though the natures of these frames vary widely. For example, when discussing deliberation, which has a long history in both practice and philosophy, Sunstein necessarily skips from high point to high point in this complex account, touching on Aristotle, Rawls, Habermas, etc. By contrast, when discussing electronic methods of aggregation, there’s comparatively little theory and considerable discussion of practice.
In all cases, however, the discussions of contemporary understanding of these methods of information aggregation were both intriguing and exciting. In particular, Sunstein’s discussion of the myriad ways prediction markets can be applied—and the fact that they outperform experts in many areas—was worthy of serious consideration. That a single mechanism is used for predicting events as diverse as presidential elections and internal product launches is intriguing. I found myself wanting to harness prediction markets for new areas in the arts or even in social and personal arenas. Likewise, the possibilities inherent in what Sunstein called “collaborative filtering”—finding parallels between books purchased or movies watched, as Amazon and Netflix do—have clearly just begun to be tapped. Such associational thinking could be used to multiply potential for product innovation or even to craft narratives within an art form.
Sunstein is clearly both committed to the potential inherent in collective understanding and excited by the revolution he sees unfolding in cyberspace or in prediction markets. However, he’s not blindly carried away by these processes. The chapter on deliberation is titled “The Surprising Failure of Deliberating Groups,” and the following chapter is called simply “Four Big Problems.” The gaffes, gaps and biases described there are deeply daunting and almost universally human. Other related failings are detailed in the discussion of the blogosphere, with the result being a book that works hard to be balanced.
While I trust Sunstein’s intent, I do not always trust his success rate. One of the potential failings he describes in deliberation is what he calls a “reputational cascade” in which people agree on a specific answer or point because they are part of a group and want to retain their social standing in it and essentially smother their objections or contradictory knowledge. Sunstein also describes the sort of cascade that follows a successful political movement and how blind people are to how they’re being carried along by the crowd. To a certain degree, I think that’s happening here. Sunstein sees so much rich potential in these emerging practices that even when he’s trying to see the objections, he fails to do so fully. What effect does Wikipedia have on creativity or does Google have on memory? What happens to older forms of collective knowledge transfer such as tradition? I would have loved to see more on areas like these as well as sharper distinctions about just what knowledge is.
However, those objections and desires don’t take anything away from Infotopia, which should start you speculating on what might be and reflecting on how the groups you belong to make decisions.
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