Who Matters Less Than Where, When, Why, with Whom…

I didn’t pay too much attention to this post from Tyler Cowen but wow am I having trouble getting it out of my head:

This position can be seen as a variation on the theme of the “strong situation hypothesis” (Cooper and Withey, 2009).  This hypothesis, based on the work of Mischel (1977), proposes that personality differences are especially like to be outwardly expressed in “weak” situations offering no clear situational clues and a wide range of possibilities as to how to behave.  Conversely, individual differences are expected to have less room for expression in “strong” situations where the choice of behavioral outcomes is severely limited and where everyone is bound to behave in a similar way.…Thus, individual risks could play a magnified role in highly disadvantaged neighborhood contexts.

That is from Tama Leventhal, Véronique Dupéré, and Elizabeth A. Shuey, “Children in Neighborhoods,” In Handbook of Child Psychology and Development Science, edited by Marc H. Bornstein and Tama Leventhal. New York: Wiley, 2015, p.520, academically gated link here, an excellent and consistently interesting survey piece complementing the recent economic studies by Chetty and others.

Ungated Cooper and Withey is here (pdf), also worth your time.  Here is a related Wikipedia entry, perhaps not as clear as it might be.

Circumstances drive actions. Most people behave the same when driving down the highway. I like to think that presidents of the United States are all mostly interchangeable with defeated hopefuls.

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Where the tech boom booms biggest

The biggest opportunities are provided by the Chinese economy’s egregious inefficiency, a legacy of decades of state capitalism. “China has more old-economy, non-transparent and unreasonably profitable firms than does America…the streets are just paved with gold for disrupters,” says Mr Lee.

Lei Jun puts it more colourfully: “Even a pig can fly if it is in the middle of a whirlwind.”

That’s from the economist. The day will come when the largest share of innovation will come from China.

But there will also be way more innovation generally. That’s the most important fallacy we make about economics and innovation. It’s a positive sum game, there is no such thing as a winner in any economically meaningful sense.

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The Engineering Case For Alien Monsters

The net result is that big organisms on Earth are just noticeably sluggish compared to small ones. But big organisms don’t have to be sluggish, that is just an accident of the engineering failures of Earth biology. If there is a planet out there where biology has figured out how to efficiently scale its blood vessels, such as by using continuous pumps, the organisms on that planet will have fewer barriers to growing large and active. Efficiently designed large animals on Earth could easily have metabolisms that are thousands of times faster than in existing animals. So, if you don’t already have enough reasons to be scared of alien monsters, consider that they might have far faster metabolisms, and as a result be very large.

This seems yet another reason to think that biology will soon be over. Human culture is inventing so many powerful advances that biology never found, innovations that are far easier to integrate into the human economy than into biological designs. Descendants that integrate well into the human economy will just outcompete biology.

That’s Robin Hanson. There’s also an interesting discussion on the relationship between biomass density and aggregate energy consumption complete with some back of the envelope model building a la Hamming. Biomass being the sum of all living things in an area.

Anyway, my walking around mental model of human progress is that the engineering achievements of nature are underrated. Take for example a story from this week’s economist where researchers are trying to break the a water speed record by replacing a propeller with a fin!

But don’t mix stocks and flows. Sure we’re frustrated that we can’t replicate or enhance some systems but look around. Much (most) of what we’ve built for ourselves exists because it’s better than what nature gave us. There’s always a new frontier to conquer and isn’t it fun to focus on the drama of the cutting edge.

Yet we’ve come a long, long way.

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China Roundup

The Chinese las Vegas is doing ok.

The Chinese rustbelt is not. At that link we get this map that helps drive home the point that this is a big regional economy.


Here is Scott Sumner

2.  People forget that until recently China had 10% trend growth, so when it goes from 14% to 7%, that’s a big slowdown.  People also forget that some sectors of the Chinese economy are probably growing smoothly.  Health care, college education, subways rides (which are constrained by capacity), etc.  So if the overall RGDP growth rate slows from 14% to 7%, and some sectors are growing smoothly at 10%, then the cyclical sectors are slowing extremely rapidly.  And the cyclical sectors are also the commodity intensive sectors.  You could easily see lots of industry data that seems inconsistent with a 7% RGDP growth rate, during a cyclical slowdown.

And, boom, Michael Pettis

But every “growth miracle” ends up following the same credibility path, with what once seemed an unending stream of sophisticated and dedicated leaders at every level of policymaking suddenly and unexpectedly becoming an administration of clunky, incompetent bureaucrats, as foolish as the rest of us. When the miracle country outperforms expectations year after year during the expansion phase, we assume that brilliant policymaking is the cause, rather than– more appropriately, as I will explain– inverted balance sheets. When this same balance sheet inversion subsequently causes the economy sharply to underperform expectations during the contraction period, our admiration for policymakers quickly turns into contempt for their incompetence, usually tinged with bitterness that our forecasts turned out so magnificently wrong.

And more..

I believe, however, that without a massive and fairly unlikely transfer of wealth from the state sector to the household sector, the average Chinese GDP growth rate under Xi Jinping cannot exceed 3-4%.

This is what had surprised me most, which is that the massive state transfer hasn’t happened, to my personal financial detriment.

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Where Innovation Goes to Die

Steve Blank quoting a friend of his:

“Most of the time our attempts at innovation result in “innovation theater” – lots of motion (memos from our CEO, posters in the cafeteria, corporate incubators) but no real change. We were once a scrappy, agile and feared organization with a “can-do” attitude. Now most people here don’t want to rock the boat and simply want do their job 9 to 5. Mid-level bureaucrats kill everything by studying it to death or saying it’s too risky. Everything innovative I’ve accomplished has taken years of political battles, calling in favors, and building alliances.”

Startups are high status these days. It’s a remarkable characteristic of our times. No surprise outsiders want to capture some of this status by adopting the startup lingo. In the end, companies optimized for execution (not innovation) carry on as usual. They have no business innovating, really, it would kill their very valuable incumbency. For a culture of moon shot gambling? Makes no sense. Try this from Horace Dediu on Steve Ballmer:

Microsoft ascended because it disrupted an incumbent (or two) and is descending because it’s being disrupted by an entrant (or two). The Innovator’s Dilemma is very clear on the causes of failure: To succeed with a new business model, Microsoft would have had to destroy (by competition) its core business. Doing that would, of course, have gotten Ballmer fired even faster.

Steve Ballmer’s only failing was delivering sustaining growth (from $20 to over $70 billion in sales.) He did exactly what all managers are incentivized to do and avoided all the wasteful cannibalization for which they are punished.

If anything, Steve Ballmer avoided The Innovator’s Curse. Being successful with new market innovations would probably have led to an even shorter tenure. Destroying prematurely the pipeline of Windows in favor for a profit-free mobile future would have been a fireable offense. Where established large companies are concerned, markets punish disruptors and reward sustainers.

Steve Ballmer will not be remembered as favorably as the man who created Microsoft. But at least he won’t be remembered as the fool who killed it. That epitaph is reserved for his successor.

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Grouping is Hard

Nokia was a phone company, blackberry was a smartphone company. They were both destroyed by a computer company called Apple, making phones. Each company’s products performed a similar service for consumers but production at these companies wasn’t at all similar. These companies got good at systematizing the multitude of tradeoffs that go into scale production. In the vernacular of Steve Blank, they were optimized to execute a very narrow business model. If a different mix of tradeoffs comes along, they were dead. And they are dead.

Yet we are surprised. We are surprised because we have grouped these companies together (PHONES) when they aren’t actually very much alike. Grouping is hard when it really matters.

A better grouping might have been (?): Nokia made voice transmission devices over wireless networks. Blackberry made data transmission devices optimized for efficiency. They were beat by a company that was still happy to (again channeling Steve Blank) continue searching for new business models. Here’s a neat quote from Steve:

The large companies that survive rapid technology and/or platform shifts are often run by founders, (Jeff Bezos at Amazon, Steve Jobs at Apple, Larry Page at Google, Larry Ellison at Oracle) or faced with an existential crisis and forced to change (Satya Nadella at Microsoft) or somehow have miraculously retained an innovation culture through multiple generations of leadership like W.L. Gore.

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Your Grandfather’s Model (Is Your Model too)

From John Taylor’s post on the fed’s model error:

The solid black line plots changes in the estimated sacrifice ratio of unemployment to changes in inflation implied by the model over 64 vintages.


Could easily be an actuary’s pricing model. That’s overfitting, folks. Responding to specific (recent) data points without and real generalizability. We must be humble in what we can know about very complex things.

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