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How and why bitcoin will plummet in price. There’s a lot of thinking in this piece and, unfortunately, a lot of econo-jargon, which Cowen is wont to do from time to time.

For purposes of argument, let’s say that a year from now Bitcoin is priced at $500.  Then you want some Bitcoin, let’s say to buy some drugs.  And you find someone willing to sell you Bitcoin for about $500.

But then the QuitCoin company comes along, with its algorithm, offering to sell you QuitCoin for $400.  Will you ever accept such an offer?  Well, QuitCoin is “cheaper,” but of course it may buy you less on the other side of the transaction as well.  The QuitCoin merchants realize this, and so they have built deflationary pressures into the algorithm, so you expect QuitCoin to rise in value over time, enough to make you want to hold it.  So you buy some newly minted QuitCoin for $400, and its price springs up pretty quickly,  at which point you buy the drugs with it.  (Note that the cryptocurrency creators will, for reasons of profit maximization, exempt themselves from upfront mining costs and thus reap initial seigniorage, which will be some fraction of the total new value they create, and make a market by sharing some of that seigniorage with early adopters.)

Next is a neat Cochrane piece commenting on a WSJ article on alternative lenders springing up to fill in for banks that don’t want to lend. Here’s the key bit:

But there is nothing that stops a bank from using new sources of information, streamlining loan approvals and so forth. So if regular banks are not doing it, and if new businesses that want to serve this market are organizing as something other than new “banks,” it raises the interesting question, what’s wrong with regulation or competition in banking?

There is a lot of talk about how banks “don’t want to lend”. Why? Cochrane’s my favorite blog these days.

Lastly, Felix Salmon with a great piece on Netflix’s pivot:

…the studios can watch the Netflix share price as easily as anybody else, and when they see it ending 2013 at $360 a share, valuing the company at well over $20 billion, that’s their sign to start raising rates sharply during the next round of negotiations. Which in turn helps explain why Netflix is losing so many great movies.

As a result, Netflix can’t, any longer, aspire to be the service which allows you to watch the movies you want to watch… if you give Netflix a list of all the movies you want to watch, the proportion available for streaming is going to be so embarrassingly low that the company decided not to even give you that option any more.

…Netflix, then, no longer wants to show me the things I want to watch, and it doesn’t even particularly want to show me the stuff I didn’t know I’d love. Instead, it just wants to feed me more and more and more of the same, drawing mainly from a library of second-tier movies and TV shows, and actually making it surprisingly hard to discover the highest-quality content.

“You shouldn’t believe what I say when I’m with the rest of the kids. Nobody acts sincere.”

So says Natalie Wood’s character Judy in this celebrated movie about teen angst released fifty-eight years ago. Is this a great movie?

It’s probably impossible to evaluate it on its own merits. Budding star James Dean died a month before the release, catapulting himself and the film to mythical status. We learn from Wikipedia that (even before his death):

“When production began, Warner Bros. considered it a B-movie project, and Ray used black and white film stock. When Jack Warnerrealized James Dean was a rising star and a hot property, filming was switched to color stock and many scenes had to be reshot in color.”

I’d strengthen that point. The movie is for the most part silly; Dean carries it.

An early scene makes the point (you can watch it here). Jim (Dean) notices Judy, who is initially unimpressed, but we see Dean’s a cool, charismatic dude. Judy’s mild contempt rings hollow in the face of Jim’s bemused indifference. One imagines her description of him to Buzz as the “new disease” to be more plaintive than smug. By the end of that day she’ll have changed her mind.

The movie is actually mostly set within that single day, the first day of school for Jim Stark who recently moved to the area. Like many kids, he’s embarrassed by his parents and is really sensitive about being picked on. After Judy catches his eye, her boyfriend Buzz bullies Jim and challenges him to a knife fight (!). No cutting, you see, just sticking. Jim wins by knocking Buzz’s knife away. Buzz then challenges Jim to that famous game of chicken and the rest of the movie follows its tragic consequences.

We see a lot of B-strength writing in the film’s pandering to cultural talking points of the day. Here is Roger Ebert’s review:

In the early 1950s, his unfocused rage fit neatly into pop psychology. The movie is based on a 1944 book of the same name by Robert Lindner, and reflected concern about “juvenile delinquency,” a term then much in use; its more immediate inspiration may have been the now-forgotten 1943 book A Generation of Vipers, by Philip Wylie, which coined the term “Momism” and blamed an ascendant female dominance for much of what was wrong with modern America. “She eats him alive, and he takes it,” Jim Stark tells the cop about his father.

Weak writing seeks to confirm the flawed intuition of an audience. It takes an ambitious film to challenge it and a great film to change it. It’s easy to say “kids are crazy these days” and point to whatever fad happens to be in the news as evidence. The thing is that kids have always seemed crazy and always will. Have you seen this study (or another like it)? Adolescent Brains Biologically Wired to Engage in Risky Behavior:

“Our results raise the hypothesis that these risky behaviors, such as experimenting with drugs or having unsafe sex, are actually driven by over activity in the mesolimbic dopamine system, a system which appears to be the final pathway to all addictions, in the adolescent brain,” Poldrack said.

These kids weren’t ‘crazy’, the were just bored and happy to risk their life for an edge in the status game of teen-hood. Jim, for all his protagonist’s self-awareness, is incensed by simple name-calling of “chicken”. For a teenager, humiliation and death are frightfully close together in significance before an accident.

And yet the film is capable of nuance. The ultimate act of madness, the game of chicken, had a distinctly non-crazy, almost nonchalant emergence. There wasn’t a grudge between Buzz and Jim: “I like you, you know?”, says Buzz at one point. “Then why are we doing this?” asks Jim. “We got to do something. Don’t we?” Same today. Buzz can’t eject because his sleeve gets caught and over the cliff he goes.

The film misses a chance to dwell a bit on the effect this would have had on the kids. There are glimmers of fear and shock and remorse, of course. And the boys worried about Jim talking frankly with the police rings true to me.

I was actually surprised at how modern the film felt and looked; much of our society’s norms and visible technology was in place then in the 50s. It makes me wonder, how far back could such a movie been made?

For technology, probably not long before. But I suspect that its plot and theme of parental angst over crazy teens would probably resonate with any group of middle class families back to the dawn of time. Try reading Lermontov’s *A Hero of Our Time* a mid-19th century treatment of similar modern themes.

Not starving? Not at war? Got some serious leisure on your hands? Then it doesn’t matter when you were born: you probably whine to your friends about how the kids these days just don’t get it.

Here’s his Graph of the year:

I see a comparison of an aggregate with an average and I get annoyed. I’m not disputing the point so much, really, but I feel like this is contrived to give us a hockey stick shape to freak out about.

Maybe the conclusion wouldn’t change comparing average to average. Maybe the data isn’t available to do an average of student debt among graduates.

But surely some acknowledgment that there probably more students every year is in order here.

Sherlock Holmes is now in the public domain. Surprised? Here’s Wikipedia:

The Act extended these terms to life of the author plus 70 years and for works of corporate authorship to 120 years after creation or 95 years after publication, whichever endpoint is earlier.^[1] Copyright protection for works published prior to January 1, 1978, was increased by 20 years to a total of 95 years from their publication date.

This law, also known as the Sonny Bono Copyright Term Extension Act, Sonny Bono Act, or (derisively) the Mickey Mouse Protection Act,^[2] effectively “froze” the advancement date of the public domain in the United States for works covered by the older fixed term copyright rules. Under this Act, additional works made in 1923 or afterwards that were still protected by copyright in 1998 will not enter the public domain until 2019 or afterward (depending on the date of the product) unless the owner of the copyright releases them into the public domain prior to that.

Everytime I read something about copyright I come away disgusted with its power and scope.

Next up Gordon Ramsay teaches us how to scramble eggs. He’s a compelling celebrity chef, I’ll give him that.

Also see this trick using onions to cook fried eggs.

I found this immensely depressing:

The number of black immigrants living in the US has increased 13-fold from 1970 to 2010, increasing their share of the black population from 1% to 10%. Black immigrants’ labor market outcomes surpass those of native blacks. This paper determines in how far the relative success of black immigrants is passed on to the second generation. While blacks of the second generation have equal or higher education and earnings levels than the first generation, the return on their unobservable characteristics is converging to that of native blacks. Race premia are put into a broader context by comparing them to Hispanics, Asians, and whites. Blacks are the only group that experiences a decrease in residual earnings when moving from the first to the second generation. Black immigrants do not only converge to native blacks across generations but also within a generation. For Asians and Hispanics, residual earnings decrease monotonically with age of immigration. For blacks, the residual earnings-age of immigration profile is upward sloping for those immigrating before the age of 15. Convergence across generations is mostly driven by low-educated second generation blacks that drop out the labor force in greater numbers than low-educated first generation immigrants do. Similarly, convergence within a generation is mostly driven by low-educated blacks who immigrate when they are young dropping out of the labor force in greater numbers than those who immigrate when they are older. A social interactions model with an assimilation parameter that varies by age of immigration helps explain this phenomenon. When making their labor force participation decision, immigrant men of all races, but not women, generally place more weight on the characteristics of natives the earlier they immigrate.

Via MR

Here is a link to Hal Varian’s latest paper, which I haven’t read. Here is Kling:

When confronted with a prediction problem of this sort an economist would think immediately of a linear or logistic regression. However, there may be better choices, particularly if a lot of data is available. These include nonlinear methods such as 1) neural nets, 2) support vector machines, 3) classification and regression trees, 4) random forests, and 5) penalized regression such as lasso, lars, and elastic nets.

In one of his examples, he redoes the Boston Fed study that showed that race was a factor in mortgage declines, and using the classification tree method he finds that a tree that omits race as a variable fits the data just as well as a tree that includes race, which implies that race was not an important factor.

I’m no fan of linear regression but at least with linear regression I know what assumptions I’m making. Multivariate linear regression is getting into scary territory but I retain the barest of grips.

Neural nets, which I’ll admit I’ve only done in a classroom setting, are a black box. Take the “proof” that Varian offers that the Boston Fed study was wrong-footed: “I can make a model with different variables that interact in ways I don’t understand that works!”. I say, holy cow, how do you have any idea you’re right.

Now I’ve not read the Boston Fed study and I haven’t read Varian’s paper or any paper he may have produced on his reanalysis of that dataset so I’m basing speculation purely on Kling’s characterization of the result.

But this sort of process and conclusion is familiar and will become more so: using really complicated tools to analyze really complicated datasets and interpreting the results in really simple ways.

Welcome to the era of big data. We are incapable of understanding anymore more than the simplest of descriptions of a dataset: mean, median, mode, percentiles. Do you understand what variance is? I sure don’t. But if can calculate it easily sometimes I can do some neat tricks with associated statistics as long as I have some underlying intuition about the data.

Multi-dimensional, massive datasets are completely non-intuitive. In catastrophe reinsurance we work every day with gigantic datasets and black box models for measuring the risks of hurricanes and earthquakes.

I like to say that the companies that build these models have a great business: they’ve built a tool that is completely non-falsifiable by humans. There are literally millions of random variables in those catastrophe models and isolating and analyzing the error is, if not impossible, at least impractical. You can’t even compare the model result of an actual historical event (say, Katrina or Sandy) with real claims data. From what I can tell talking to them about this kind of exercise, even the model builders themselves calibrate results on an aggregate basis. 

Rule #1 in my practical guide to statistics: you don’t understand aggregates.

Claims of mastery of big data modeling are hard to believe.

Here David Brooks is right:

I figure that unless you are in the business of politics, covering it or columnizing about it, politics should take up maybe a tenth corner of a good citizen’s mind. The rest should be philosophy, friendship, romance, family, culture and fun.

But he obviously doesn’t watch enough TV:

I wish our talk-show culture reflected that balance, and that the emotional register around politics were more in keeping with its low but steady nature.

The vast majority of TV has nothing to do with politics. Many lament this, of course, because they worry about disengagement with the political process. “If we don’t watch ’em, who will?”.

Wars aside, regular people who don’t care about politics only tune in when they get screwed over by something. You could argue (I’d sure like to) that it’s in politicians’ best interests to do as little as possible lest they rouse the ire of the silent multitude.

Here’s a good abstract:

Levitt and List (2007) conjecture that selection pressures among business people will reduce or eliminate pro-social choices. While recent work comparing students with various adult populations often fails to find that adults are less pro-social, this evidence is not necessarily at odds with the selection hypothesis, which may be most relevant for behavior in cutthroat competitive industries. To examine the selection hypothesis, we compare students with two adult populations deliberately selected from two cutthroat internet industries — domain trading and adult entertainment (pornography). Across a range of indicators, business people in these industries are more pro-social than students: they are more altruistic, trusting, trustworthy, and lying averse. They also respond differently to shame-based incentives. We offer a theory of reverse selection that can rationalize these findings.

One thing I was always struck with about the business I work in is how similar senior people at each company are. For the most part it doesn’t matter whether you’re an actuary, accountant or producer, everyone, particularly when working within their domain of expertise, is polite, smart, friendly, conscientious, well dressed and even reasonably pleasant looking.

Insurance, like others, is a social business. What’s more, the insurance industry is obsessed with moral hazard and has evolved a complex set of interrelationships among companies to suss it out. Only humans can tell if humans are playing for suckers.

These people need to get along, often while competing against each other viciously. It’s a bizarre setup and one that requires serious social skill.

For a different take, he authors of the study might have taken a hard look at asocial trading jobs. Behind the bloomberg terminal an asshole is no different than a saint and money follows talent.

But in most businesses, even the most competitive, success depends on getting along.

Help us live longer:

A few years back, Robert Ohsfeldt of Texas A&M and John Schneider of the University of Iowa asked the obvious question: what happens if you remove deaths from fatal injuries from the life expectancy tables? Among the 29 members of the OECD, the U.S. vaults from 19th place to…you guessed it…first. Japan, on the same adjustment, drops from first to ninth.

More here.

Oh, and driverless cards are actually being used at Heathrow:

Milton Keynes, a town north of London, has announced that it will be deploying 100 driverless pods (officially known as ULTra PRT transport pods) as a public transportation system. A similar system has been running for two years at Heathrow airport. The plan is to have the system up and running by 2015, with a full rollout by 2017. The move marks the first time that self-driving vehicles will be allowed to run on public roads in that country.

My initial thought about driverless cars was that the tort liability system will kill the whole idea. But now I’ve heard more big thinking about what it could possibly mean to have driverless cars and I’m starting to think I might have gotten it backwards.

Consider how much of what we think of as the problems with cars are not problems with the technology but problems with human behavior as expressed through the technology: poor reaction time, sleepiness, texting, drunk driving.

We drive slower, make more mistakes and keep our cars nearer to us than we would with machines behind the wheel. Most importantly, we hurt more people than machines would.

Auto insurance is about 40% of the insurance market in the US and that is mostly driven by liability insurance: what you buy to pay for harm you do to others from behind the wheel. If less harm gets done, the market will collapse, in the sense that claims cost and premium will plummet.

Good for us all, of course, but a titanic shift in the insurance landscape.