A Nobel Laureate of the world
Any expectation of spotting the hottest trends in ultra-modern Italian interior design are nipped in the bud when entering Michael Spence’s Milan apartment. There’s no majestic view of the Teatro alla Scala, only a huge tipi-tent in the living room, with Lego scattered across the floor. Here in Italy, where Spence lives with his wife Giuliana and his two youngest kids, he’s in easy-going dad-mode as opposed to the jet-setting Nobel Laureate version.
Once all the toy fire trucks are parked and the Lego towers are set aside, Spence highlights the benefits of frequenting so many countries. "For a lot of people, reading about life in Peru is sort of like reading about people on Mars," he says, and recommends that everybody explores as many places and people as possible because, "It doesn’t have to be like that".
Can we design a framework that leads to growth in every country?
Spending more time in the air than on solid ground, it’s not surprising that Spence dedicates himself to global belonging. Right after receiving the Nobel Prize, he took on a big project initiated by the World Bank. The challenge: to constitute a framework for growth. "We went and looked for countries that had grown at a very high rate. Seven percent or above for 25 years and more", he says, looking back to his start as a "newbie" macroeconomist.
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How should Europe rewrite its rules?
Portugal, Spain, Italy and, above all, Greece are the countries that went sailing into oblivion, as he describes it. "We don’t have inflation to accelerate the process of reducing the public debt overhang," Spence says. "Without meaning to, we created an economic structure that’s very short on adjustment mechanisms and therefore it’s very difficult to see anything other than a prolonged period of rebalancing that takes a long, long, time."
Even if there might be better long-term answers, as he acknowledges, it leads to the immediate important question: what are we going to do now?
How can we close information gaps?
"Sometimes", says Spence, "there are ways to solve the problem; with contracts, for example, which guarantee the maintenance of the product for a certain time. The low-end quality spectrum won’t do that. So there is a way to distinguish between quality and low-end products," he explains.
Signaling and screening, the major part of his work, is sometimes strategic. It sounds academic, but in real-life it’s actually common practice. People study at elite universities like Stanford to expand their job opportunities later on. But those students, in Spence’s opinion, aren’t being strategic. They’re just responding to a set of incentives that already exist. "If you have the ability to get into them", Spence emphasizes, "what you get in return is really two things: one is what you learn, and the other is the signaling effect."
When information gaps are closed, success is automatic
It all sounds easy and logical; but the real breakthrough actually rests within the every-day application that everybody takes for granted. "Let’s take a modern example," he introduces, to show how these theoretical "agents" function in our daily routine. "Airbnb", he exclaims. "Airbnb does two things: they create a market using a network structure and they solve an informational problem. Because I, as a host of an apartment, I am not in the market all that much, and you as a guest may not be in the market that much, but you’re in it often enough for the central player, who keeps very big databases, to be able to evaluate you. And so the other crucial part of these platforms is the two-way evaluation system, closing informational gaps."
Can digital technology become a threat?
Using digital technology has not only had a fairly profound effect on the informational structure of markets, but also on their very existence. Spence, who keeps his tablet close at hand, knows that many people his age are digitally illiterate. But, in his opinion, the "increasing importance" of social networks is undisputed.
His mind wanders: "So a lot of what enables artificial intelligence is that you have very fast machines connected to very fast networks, connected to gigantic databases including images and other data. So for example the way machines recognize chairs is, they take half a second and go look at 80.000 chairs, and they have pattern recognition capabilities, and now they sort of know what a chair is the same way we do. Instead of telling a machine how to do something, you tell it on a much deeper level how to learn."
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