Yesterday was a day filled with secrets and codes. In the morning, at The Platform, we had guest speaker Columbia history professor Matthew Connelly, who came and talked to us about his work with declassified documents. Two big and slightly depressing take-aways for me were the following:
- As records have become digitized, it has gotten easy for people to get rid of archival records in large quantities. Just press delete.
- As records have become digitized, it has become easy to trace the access of records, and in particular the leaks. Connelly explained that, to some extent, Obama’s harsh approach to leakers and whistleblowers might be explained as simply “letting the system work.” Yet another way that technology informs the way we approach human interactions.
After class we had section, in which we discussed the Computer Science classes some of the students are taking next semester (there’s a list here) and then I talked to them about prime numbers and the RSA crypto system.
I got really into it and wrote up an iPython Notebook which could be better but is pretty good, I think, and works out one example completely, encoding and decoding the message “hello”.
Yesterday we were pleased to have Suresh Naidu guest lecture in The Platform. He came in and explained, very efficiently because he was leaving at 11am for a flight at noon at LGA (which he made!!) how to think like an economist. Or at least an applied microeconomist. Here are his notes:
Applied microeconomics is basically organized a few simple metaphors.
- People respond to incentives.
- A lot of data can be understood through the lens of supply and demand.
- Causality is more important than prediction.
There was actually more on the schedule, but Suresh got into really amazing examples to explain the above points and we ran out of time. At some point, when he was describing itinerant laborers in the United Arab Emirates, and looking at pay records and even visiting a itinerant labor camp, I was thinking that Suresh is possibly an undercover hardcore data journalist as well as an amazing economist.
As far as the “big data” revolution goes, we got the impression from Suresh that microeconomists have been largely unmoved by its fervor. For one, they’ve been doing huge analyses with large data sets for quite a while. But the real reason they’re unmoved, as I infer from his talk yesterday, is that big data almost always focuses on descriptions of human behavior, and sometimes predictions, and almost never causality, which is what economists care about.
A side question: why is it that economists only care about causality? Well they do, and let’s take that as a given.
So, now that we know how to think like an economist, let’s read this “Room For Debate” about overseas child labor with our new perspective. Basically the writers, or at least three out of four of them, are economists. So that means they care about “why”. Why is there so much child labor overseas? How can the US help?
The first guy says that strong unions and clear signals from American companies works, so the US should do its best to encourage the influence of labor unions.
The lady economist says that bans on child labor are generally counterproductive, so we should give people cash money so they won’t have to send their kids to work in the first place.
The last guy says that we didn’t even stop having child labor in our country until wage workers were worried about competition from children. So he wants the U.S. to essentially ignore child labor in other countries, which he claims will set the stage for other countries to have that same worry and come to the same conclusion by themselves. Time will help, as well as good money from the US companies.
So the economists don’t agree, but they all share one goal: to figure out how to tweak a tweakable variable to improve a system. And hopefully each hypothesis can be proven with randomized experiments and with data, or at least evidence can be gathered for or against.
One more thing, which I was relieved to hear Suresh say. There’s a spectrum of how much people “believe” in economics, and for that matter believe in data that seems to support a theory or experiment, and that spectrum is something that most economists run across on a daily basis. Even so, it’s not clear there’s a better way to learn things about the world than doing your best to run randomized experiments, or find close-to-randomized experiments and see how what they tell you.
When I was prepping for my Slate Money podcast last week I read this column by Matt Levine at Bloomberg on the Citigroup settlement. In it he raises the important question of how the fine amount of $7 billion was determined. Here’s the key part:
Citi’s and the Justice Department’s approaches both leave something to be desired. Citi’s approach seems to be premised on the idea that the misconduct was securitizing mortgages: The more mortgages you did, the more you gotta pay, regardless of how they performed. The DOJ’s approach, on the other hand, seems to be premised on the idea that the misconduct was sending bad e-mails about mortgages: The more “culpable” you look, the more it should cost you, regardless of how much damage you did.
I would have thought that the misconduct was knowingly securitizing bad mortgages, and that the penalties ought to scale with the aggregate badness of Citi’s mortgages. So, for instance, you’d want to measure how often Citi’s mortgages didn’t match up to its stated quality-control standards, and then compare the actual financial performance of the loans that didn’t meet the standards to the performance of the loans that did. Then you could say, well, if Citi had lived up to its promises, investors would have lost $X billion less than they actually did. And then you could fine Citi that amount, or some percentage of that amount. And you could do a similar exercise for the other big banks — JPMorgan, say, which already settled, or Bank of America, which is negotiating its settlement — and get comparable amounts that appropriately balance market share (how many bad mortgages did you sell?) and culpability (how bad were they?).
I think he nailed something here, which has eluded me in the past, namely the concept of what comprises evidence of wrongdoing and how that translates into punishment. It’s similar to what I talked about in this recent post, where I questioned what it means to provide evidence of something, especially when the data you are looking for to gather evidence has been deliberately suppressed by either the people committing wrongdoing or by other people who are somehow gaining from that wrongdoing but are not directly involved.
Basically the way I see Levine’s argument is that the Department of Justice used a lawyerly definition of evidence of wrongdoing – namely, through the existence of emails saying things like “it’s time to pray.” After determining that they were in fact culpable, they basically did some straight-up negotiation to determine the fee. That negotiation was either purely political or was based on information that has been suppressed, because as far as anyone knows the number was kind of arbitrary.
Levine was suggesting a more quantitative definition for evidence of wrongdoing, which involves estimating both “how much you know” and “how much damage you actually did” to determine the damage, and then some fixed transformation of that damage becomes the final fee. I will ignore Citi’s lawyers’ approach since their definition was entirely self-serving.
Here’s the thing, there are problems with both approaches. For example, with the lawyerly approach, you are basically just sending the message that you should never ever write some things on email, and most or at least many people know that by now. In other words, you are training people to game the system, and if they game it well enough, they won’t get in trouble. Of course, given that this was yet another fine and nobody went to jail, you could make the argument – and I did on the podcast – that nobody got in trouble anyway.
The problem with the quantitative approach, is that first of all you still need to estimate “how much you knew” which again often goes back to emails, although in this case could be estimated by how often the stated standards were breached, and second of all, when taken as a model, can be embedded into the overall trading model of securities.
In other words, if I’m a quant at a nasty place that wants to trade in toxic securities, and I know that there’s a chance I’d be caught but I know the formula for how much I’d have to pay if I got caught, then I could include this cost, in addition to an estimate of the likelihood for getting caught, in an optimization engine to determine exactly how many toxic securities I should sell.
To avoid this scenario, it makes sense to have an element of randomness in the punishments for getting caught. Every now and then the punishment should be much larger than the quantitative model might suggest, so that there is less of a chance that people can incorporate the whole shebang into their optimization procedure. So maybe what I’m saying is that arriving at a random number, like the DOJ did, is probably better even though it is less satisfying.
Another possibility to actually deter crimes would be to arbitrarily increasing the likelihood of catching people up to no good, but that has been bounded from above by the way the SEC and the DOJ actually work.
So I have been getting some feedback lately on how I always assume everyone has a crush on me. People know this is my typical assumption because I say things like, “oh yeah that guy totally has a crush on me.” And when I say “feedback,” what I mean is people joyfully accusing me of lying, or maybe just outraged by my preposterous claims, usually in a baffled and friendly manner. Just a few comments about this.
First of all, I also have a crush on everyone else. Just as often as I say “that lady has a crush on me,” I am known to say, “Oh my god I have a huge crush on her.” It’s more fun that way!
Second of all, there are all sorts of great consequences of thinking someone has a crush on you. To name a few:
- It’s not a sexual thing at all, it’s more like a willingness to think the other person is super awesome. I have crush on all sorts of people, men and women, etc. etc.. No categories left uncrushed except meanies.
- When you act like someone has a crush on you, they are more likely to develop a crush on you. This is perhaps the most important point and should be first, but I wanted to get the first point out of the way. It’s what I call a positive feedback loop.
- It makes you feel great to be around someone if they have a crush on you, or even if you just think they do.
- What’s the worst thing that can happen? Answer: that you’re wrong, and they don’t have a crush on you, but then they’ll just walk away thinking that you were weirdly happy to see them, which is not so bad, and may in fact make them crush out on you. See what I mean?
- It’s a nice world to live in where a majority of the people you run into have a crush on you. Try it and see for yourself!
I managed to record this week’s Slate Money podcast early so I could drive up to HCSSiM for July 17th, and the Yellow Pig Day celebration. I missed the 17 talk but made it in time for yellow pig carols and cake.
This morning my buddy Aaron decided to let me talk to the kids in the last day of his workshop. First Amber is working out the formula for the Euler Characteristic of a planar graph with the kids and after that I’ll help them count the platonic solids using stereographic projection. If we have time we’ll talk about duals (update: we had time!).
Tonight at Prime Time I’ll play a game or two of Nim with them.
People who celebrate the monthly jobs report getting better nowadays often forget to mention a few facts:
- the new jobs are often temporary or part-time, with low wages
- the old lost jobs, which we lose each month, were often full-time with higher wages
I could go on, and I have, and mention the usual complaints about the definition of the unemployment rate. But instead I’ll take a turn into a thought experiment I’ve been having lately.
Namely, what is the future of work?
It’s important to realize that in some sense we’ve been here before. When all the farming equipment got super efficient and we lost agricultural jobs by the thousands, people swarmed to the cities and we started building things with manufacturing. So if before we had “the age of the farm,” we then entered into “the age of stuff.” And I don’t know about you but I have LOTS of stuff.
Now that all the robots have been trained and are being trained to build our stuff for us, what’s next? What age are we entering?
I kind of want to complain at this point that economists are kind of useless when it comes to questions like this. I mean, aren’t they in charge of understanding the economy? Shouldn’t they have the answer here? I don’t think they have explained it if they do.
Instead, I’m pretty much left considering various science fiction plots I’ve heard about and read about over the years. And my conclusion is that we’re entering “the age of service.”
The age of service is a kind of pyramid scheme where rich people employ individuals to service them in various ways, and then those people are paid well so they can hire slightly less rich people to service them, and so on. But of course for this particular pyramid to work out, the rich have to be SUPER rich and they have to pay their servants very well indeed for the trickle down to work out. Either that or there has to be a wealth transfer some other way.
So, as with all theories of the future, we can talk about how this is already happening.
I noticed this recent Bloomberg View article about how rich people don’t have normal doctors like you and me. They just pay out of pocket for super expensive service outside the realm of insurance. This is not new but it’s expanding.
Here’s another example of the future of jobs, which I should applaud because at least someone has a job but instead just kind of annoys me. Namely, the increasing frequency where I try to make a coffee date with someone (outside of professional meetings) and I have to arrange it with their personal assistant. I feel like, when it comes to social meetings, if you have time to be social, you have time to arrange your social calendar. But again, it’s the future of work here and I guess it’s all good.
More generally: there will be lots of jobs helping out old people and sick people. I get that, especially as the demographics tilt towards old people. But the mathematician in me can’t help but wonder, who will take care of the old people who used to be taking care of the old people? I mean, they by definition don’t have lots of extra cash floating around because they were at the bottom of the pyramid as younger workers.
Or do we have a system where people actually change jobs and levels as they age? That’s another model, where oldish people take care of truly old people and then at some point they get taken care of.
Of course, much like the Star Trek world, none of this has strong connection to the economy as it is set up now, so it’s hard to imagine a smooth transition to a reasonable system, and I’m not even claiming my ideas are reasonable.
By the way, by my definition most people who write computer programs – especially if they’re writing video games or some such – are in a service industry as well. Pretty much anyone who isn’t farming or building stuff in manufacturing is working in service. Writers, poets, singers, and teachers included. Hell, the future could be pretty awesome if we arrange things well.
Anyhoo, a whimsical post for Thursday, and if you have other ideas for the future of work and how that will work out economically, please comment.
In the past 12 hours I’ve read two fascinating articles about the crazy world of standardized testing. They’re both illuminating and well-written and you should take a look.
First, my data journalist friend Meredith Broussard has an Atlantic piece called Why Poor Schools Can’t Win At Standardized Testing wherein she tracks down the money and the books in the Philadelphia public school system (spoiler: there’s not enough of either), and she makes the connection between expensive books and high test scores.
Here’s a key phrase from her article:
Pearson came under fire last year for using a passage on a standardized test that was taken verbatim from a Pearson textbook.
The second article, in the New Yorker, is written by Rachel Aviv and is entitled Wrong Answer. It’s a close look, with interviews, of the cheating scandal from Atlanta, which I have been studying recently. The article makes the point that cheating is a predictable consequence of the high-stakes “data-driven” approach.
Here’s a key phrase from the Aviv article:
After more than two thousand interviews, the investigators concluded that forty-four schools had cheated and that a “culture of fear, intimidation and retaliation has infested the district, allowing cheating—at all levels—to go unchecked for years.” They wrote that data had been “used as an abusive and cruel weapon to embarrass and punish.”
Putting the two together, it’s pretty clear that there’s an acceptable way to cheat, which is by stocking up on expensive test prep materials in the form of testing company-sponsored textbooks, and then there’s the unacceptable way to cheat, which is where teachers change the answers. Either way the standardized test scoring regime comes out looking like a penal system rather than a helpful teaching aid.
Before I leave, some recent goodish news on the standardized testing front (hat tip Eugene Stern): Chris Christie just reduced the importance of value-added modeling for teacher evaluation down to 10% in New Jersey.