Archive
Are Corporations People?
Recently Mitt Romney put his foot in his mouth when trying to deal with a heckler in Iowa. He said, “Corporations are people, my friend.” He’s gotten plenty of backlash since then, even though he attempted a softer follow-up with, “Everything corporations earn ultimately goes to people. Where do you think it goes?”
It makes me wonder two things. First, why is it viscerally repulsive (to me) that he should say that, and second, beyond the gut reaction, to what extent does this statement make sense?
The New York Times summed up the feeling pretty well with the statement, “…he seemed to reinforce another image of himself: as an out-of-touch businessman who sees the world from the executive suite.” Another way to say this is that the remark exposed a world view that I don’t share, and which goes back to this post containing the following:
Conservatives, for example, see business as primarily a source of social and economic good, achieved by the market mechanism of seeking to maximize profit. They therefore think government’s primary duty regarding businesses is to see that they are free to pursue their goal of maximizing profit. Liberals, on the other hand, think that the effort to maximize profit threatens at least as much as it contributes to our societies’ well-being. They therefore think that government’s primary duty regarding businesses is to protect citizens against business malpractice.
Fair enough- Mitt Romney doesn’t claim to be a liberal, after all. He was really doing us a favor by admitting how he sees things; heck, I wish all politicians would be susceptible to heckling and would go off-script and say what they actually mean every now and then.
In this way I can come to terms with the fact that Romney is essentially protective of corporations and their “human rights,” at least as an emotional response (like when discussing tax increases). But is he factually right? Are corporations equivalent to people in a legal or ethical way?
I’m no lawyer but it seems that, in certain ways, corporations are legally treated as persons, and that this has been an ongoing legal question for 200 years. In terms of political contributions, which is somehow easier to understand but maybe less systemically important, they are certainly treated like persons, in that there is no limit to the amount of money they can contribute politically (although this issue has gone back and forth historically).
Ethically, however, there seems to me to be a huge obstacle in considering corporations equivalent to people. Namely, it seems to be much easier to ascribe the rights of people to corporations than to ascribe the responsibilities of people to corporations. In particular, what if corporations behave badly and need to be punished? How do we follow through with that in a way that makes sense? Is there a death penalty for corporations? (This question originally came to me by way of Josh Nichols-Barrer, by the way)
The most obvious direct punishment we have for corporations is fines for accounting fraud or whatever, and the most obvious indirect punishment is market capitalization loss, i.e. the stock price goes down, if it’s a publicly traded company, or if not, reputation loss, which is vague indeed. However, in those cases it’s mostly the shareholders that suffer- the corporation itself, and its management, typically lives on.
Rarely, there is direct legal action against a decision maker at the company, but that certainly can’t count as a death penalty for the corporation itself, since the toxic culture which gave rise to those decisions is left intact. Even if we got serious and closed down a company, it’s not clear what effect that would have since a new legal entity could be re-formed with similar ideals and people (although the nuisance of doing this would be pretty substantial depending on the industry). But maybe that’s the best we can do: “moral bankruptcy” proceedings. Another problem with that idea is that many of the people who were in charge of the bad decisions would be the first to jump ship and go to other corporations to try again with more stealth; that’s certainly what I’ve seen happen in finance.
From my perspective, none of the punishments described above actually deter bad behavior in a meaningful way. If we treat corporations as people, then they would be people with a permanent diplomatic immunity; this doesn’t sit well with my sense of fairness or my sense of how people respond to incentives.
Open Source Ratings Model (Part 2)
I’ve thought more about the concept of an open source ratings model, and I’m getting more and more sure it’s a good idea- maybe an important one too. Please indulge me while I passionately explain.
First, this article does a good job explaining the rot that currently exists at S&P. The system of credit ratings undermines the trust of even the most fervently pro-business entrepreneur out there. The models are knowingly games by both sides, and it’s clearly both corrupt and important. It’s also a bipartisan issue: Republicans and Democrats alike should want transparency when it comes to modeling downgrades- at the very least so they can argue against the results in a factual way. There’s no reason I can see why there shouldn’t be broad support for a rule to force the ratings agencies to make their models publicly available. In other words, this isn’t a political game that would score points for one side or the other.
Second, this article discusses why downgrades, interpreted as “default risk increases” on sovereign debt doesn’t really make sense- and uses as example Japan, which was downgraded in 2002 but still continues to have ridiculously low market-determined interest rates. In other words, ratings on governments, at least the ones that can print their own money (so not Greece), should be taken as a metaphor of their fiscal problems, or perhaps as a measurement of the risk that they will have potentially spiraling inflation when they do print their way out of a mess. An open source quantitative model would not directly try to model the failure of politicians to agree (although there are certainly market data proxies for that kind of indecision), and that’s ok: probably the quantitative model’s grade on sovereign default risk trained on corporate bonds would still give real information, even if it’s not default likelihood information. And, being open-source, it would at least be clear what it’s measuring and how.
I’ve also gotten a couple excellent comments already on my first post about this idea which I’d like to quickly address.
There’s a comment pointing out that it would take real resources to do this and to do it well: that’s for sure, but on the other hand it’s a hot topic right now and people may really want to sponsor it if they think it would be done well and widely adopted.
Another commenter had concerns of the potential for vandals to influence and game the model. But here’s the thing, the point of open source is that, although it’s impossible to avoid letting some people have more influence than others on the model (especially the maintainer), this risk is mitigated in two important ways. First of all it’s at least clear what is going on, which is way more than you can say for S&P, where there was outrageous gaming going on and nobody knew (or more correctly nobody did anything about it). Secondly, and more importantly, it’s always possible for someone to fork the open source model and start their own version if they think it’s become corrupt or too heavily influenced by certain methodologies or modeling choices. As they say, if you don’t like it, fork it.
Update! There’s a great article here about how the SEC is protecting the virtual ratings monopoly of S&P, Moody’s, and Fitch.
Open Source Ratings Model?
A couple of days ago I got this comment from a reader, which got me super excited.
His proposal is that we could start an open source ratings model to compete with S&P and Moody’s and Fitch ratings. I have made a few relevant lists which I want to share with you to address this idea.
Reasons to have an open source ratings model:
- The current rating agencies have a reputation for bad modeling; in particular, their models, upon examination, often have extremely unrealistic underlying assumptions. This could be rooted out and modified if a community of modelers and traders did their honest best to realistically model default.
- The current ratings agencies also have enormous power, as exemplified in the past few days of crazy volatile trading after S&P downgraded the debt of the U.S. (although the European debt problems are just as much to blame for that I believe). An alternative credit model, if it was well-known and trusted, would dilute their power.
- Although the rating agency shared descriptions of their models with their clients, they weren’t in fact open-source, and indeed the level of exchange probably served only to allow the clients to game the models. One of the goals of an open-source ratings model would be to avoid easy gaming.
- Just to show you how not open source S&P is currently, check out this article where they argue that they shouldn’t have to admit their mistakes. When you combine the power they wield, their reputation for sloppy reasoning, and their insistence on being protected from their mistakes, it is a pretty idiotic system.
- The ratings agencies also have a virtual lock on their industry- it is in fact incredibly difficult to open a new ratings agency, as I know from my experience at Riskmetrics, where we looked into doing so. By starting an open source ratings model, we can (hopefully) avoid issues like permits or whatever the problem was by not charging money and just listing free opinions.
Obstructions to starting an open source ratings model:
- It’s a lot of work, and we would need to set it up in some kind of wiki way so people could contribute to it. In fact it would have to me more Linux style, where some person or people maintain the model and the suggestions. Again, lots of work.
- Data! A good model requires lots of good data. Altman’s Z-score default model, which friends of mine worked on with him at Riskmetrics and then MSCI, could be the basis of an open source model, since it is being published. But the data that trains the model isn’t altogether publicly available. I’m working on this, would love to hear readers’ comments.
What is an open source model?
- The model itself is written in an open source language such as python or R and is publicly available for download.
- The data is also publicly available, and together with the above, this means people can download the data and model and change the parameters of the model to test for robustness- they can also change or tweak the model themselves.
- There is good documentation of the model describing how it was created.
- There is an account kept of how often different models are tried on the in-sample data. This prevents a kind of data fitting that people generally don’t think about enough, namely trying so many different models on one data set that eventually some model will look really good.
The Life Cycle of a Hedge Fund
When people tell me they are interested in working at a hedge fund, I always tell them a few things. First I talk about the atmosphere and culture, to make sure they would feel comfortable with it. Then I talk to them about which hedge fund they’re thinking about, because I think it makes a huge difference, especially how old a hedge fund is.
Here’s the way I explain it. When a hedge fund is new, a baby, it either works or it doesn’t. If it doesn’t, you never even hear about it, a kind of survivorship bias. So the ones you hear about work well, and their founders do extremely well for themselves.
Then the hedge fund hires a bunch of people, and this first round of people also does well, and they start filling up the ranks of MD’s (managing directors). Maybe at this point you’d say the hedge fund is an adolescent. Once you have a bunch of MD’s that are rich and smart, though, they become pretty protective of the pot of money they generate each year, especially if the pot isn’t as big as it once was, because of competition from other hedge funds.
However, this doesn’t always mean they stop hiring. In fact, they often hire people at this stage, young, smart, incredibly hard working people, who are generally screwed in the sense that they have very little chance of being successful or ever becoming MD. This is what I’d term an adult hedge fund. They have complicated rules which make sense for the existing MD’s but which keep new people from ever succeeding.
For example, when you get to a hedge fund, you start being assigned models to work on. You learn the techniques and follow the rules of the hedge fund, like making sure you don’t bet on the market, etc. If your model starts to look promising, they make sure you are not “remaking” an existing model that is currently being used. That is to say, they make sure, either by telling you what to do or asking you to do it yourself, that your bets are essentially orthogonal (in a statistical sense) to the current models. This often has the effect of removing the signal that your model had, or at least removing enough of it that your model no longer is statistically significant to go into production.
In other words, if the existing models are a relatively large collection, that perhaps spans the space of “current models that seem to work in the way we measure models” (I know this is a vague concept but I do think it means something), then you are kind of up shit’s creek to find a new model. By contrast, if you happened to start at a young hedge fund, or start your own hedge fund, then your model couldn’t be redundant, since there wouldn’t be anything to compete with it.
The older hedge funds have lots of working models, so there are lots of ways for your new, good-looking model to be swatted down before it has a chance to make money. And the way things work, you don’t ever get credit for a model that would have worked if there had been fewer models in production. In fact you only get credit if you came up with a new model which made shit tons of money.
Which is to say, under this system, the founders and the guys brought in during the first round of hiring are the most likely to get credit. Even if an MD retires, their working models don’t die, since they are algorithmic and they still work. But the money they generate goes into the company-wide pot, which is to say mostly goes to MD’s. So the MD’s have no incentive to change the system.
It also has another consequence, which is that the people hired in the second or further rounds slowly realize that their models are perfectly good but unused, and that they’ll never get promoted. So they end up leaving and starting their own funds or joining young funds, just so they can run the same models. So another consequence of adult hedge funds is that they spawn their own competition.
The only way I know of for a hedge fund to avoid this aging process is to never hire anyone after the first round. Or maybe to hire very few people, slowly, as the MD’s retire and as the models stop working and you need new ones, to be sure that the people they hire have a chance to succeed.
Wall Street versus us
There have been two articles in the past few days which address the mentality of people working on Wall Street versus the rest of us.
First, we have this article from William Cohen, posted on Bloomberg.com, which is the first part of a series entitled, “Ending the Moral Rot on Wall Street.” This first part doesn’t contain much new; it goes over just how obnoxious and easy to hate the various Goldman Sachs assholes were when they packaged and sold mortgage debris and then emailed their friends about how much money they stood to make. And the second (and perhaps further) parts promise to explain how we are going to address the corruption and greed. My complaint, which is totally unfounded since I haven’t read the next parts, is that this guy is not disagreeing well. In other words, he’s setting up the guys on Wall Street to be monstrous and ethically vapid. This attitude is not going to help really understand the situation, nor will it lend itself to satisfying solutions. Here’s an example of the kind of “they are monsters” prose that probably won’t help:
These crimes are being committed, he said, by people who “have already made more money than could ever be spent in one lifetime and achieved more impressive success than could ever be chronicled in one obituary. And it begs the question, is corporate culture becoming increasingly corrupt?”
Yes, it certainly does raise that question.
Second, we have this blog post by Mark Cuban, which was originally posted in 2010 but is still relevant. In it, an effort is made to understand the actual mentality of the traders on Wall Street. Namely, they are framed as hackers:
Just as hackers search for and exploit operating system and application shortcomings, traders do the same thing. A hacker wants to jump in front of your shopping cart and grab your credit card and then sell it. A high frequency trader wants to jump in front of your trade and then sell that stock to you. A hacker will tell you that they are serving a purpose by identifying the weak links in your system. A trader will tell you they deserve the pennies they are making on the trade because they provide liquidity to the market.
I recognize that one is illegal, the other is not. That isn’t the important issue.
I agree with this characterization, and moreover I applaud the effort to understand the culture. These guys actually do think they are playing fairly within the context of their “game” (and they do care that it’s legal). To change their mindset we need to actually change the rules of the game, not just complain that they are corrupt, because, like in a religious disagreement, they can easily dismiss such talk as irrelevant to their lives.
Going back to the first article, it says:
That Wall Street executives have been able to avoid any shred of responsibility for their actions in the years leading up to the crisis speaks volumes not only about an abject ethical deterioration but also about the unhealthy alliance that exists between the powerful in Washington and their patrons in New York. Our collective failure to demand redress against a Wall Street culture that remains out of control is one of the more troubling facts of life in America today.
I agree that we do need to demand redress, but not against a culture’s ethical deterioration, which is just far too vague, but rather against individual corrupt actions. In other words we need to make the punishments for well-defined evil deeds clear and we need to follow through with the consequences. In order to do this we need to demand transparency so we can start to even define evil deeds. This means some system of understanding the models that are being used, and the risks being taken, and a market consensus that the models are sufficient. It means the actual threat of losing actual money, or even going to jail, if the models being used are crappy or if it turns out you were lying about the risks you were taking – or even if you were ignorant of them.
Monday morning reading list
I’m happy to have found three really interesting articles in the New York Times this morning that I thought I’d share.
First, there’s a book review of “The Theory That Would Not Die,” a book about the history of Bayes’ law and the field of Bayesian statistics. It’s always seemed silly (and amusing) to me that there are such pissing contests between different groups of statisticians (the Bayesians versus the Frequentists), but there you are. And I guess this book is here to explain that partly it’s due to the fact that nobody took Bayes’ law seriously, so the people using it were constantly having to defend themselves. Honestly I’m just psyched that a math book is being reviewed in the first place, and written by a woman no less.
Second, there’s an interesting article about A.I.G. suing Bank of America over the mortgage bonds, with excellent background for how little litigation is actually happening due to the credit crisis, especially by our government. Reading between the lines, I would say we could summarize this attitude by our government as along the lines of the following: “Oh wow, those models are complicated. Since I don’t understand them and I don’t expect you to, even though you relied on them for your business, I will let you off the hook. After all, you can’t go to jail for not understanding math!”.
Finally, there’s a really scathing description here of how the politicians are rendering the S.E.C. impotent by giving them too much to do, taking away their power and resources, and generally trying to get micromanaging control over how they do their thing. True, it’s written by a former chairman of the S.E.C., but it’s still not a convincing way to create a powerful regulator (if that’s what anyone wants).
Data Viz
The picture below is a visualization of the complexity of algebra. The vertices are theorems and the edges between theorems are dependencies. Technically the edges should be directed, since if Theorem A depends on Theorem B, we shouldn’t have it the other way around too!
This comes from data mining my husband’s open source Stacks Project; I should admit that, even though I suggested the design of the picture, I didn’t implement it! My husband used graphviz to generate this picture – it puts heavily connected things in the middle and less connected things on the outside. I’ve also used graphviz to visualize the connections in databases (MySQL automatically generates the graph).
Here’s another picture which labels each vertex with a tag. I designed the tag system, which gives each theorem a unique identifier; the hope is that people will be willing to refer to the theorems in the project even though their names and theorem numbers may change (i.e. Theorem 1.3.3 may become Theorem 1.3.4 if someone adds a new result in that section). It’s also directed, showing you dependency (Theorem A points to Theorem B if you need Theorem A to prove Theorem B). This visualizes the results needed to prove Chow’s Lemma:
Adam Smith made me buy a Kindle
When I was pregnant with my third son, and working at D.E. Shaw, I got really into reading Adam Smith’s seminal work “Wealth of Nations” on the subway rides to and from work. Once the baby came, though, the problem was that the book is huge, like 1,200 pages, and impossible to read while breastfeeding. In my frustration, and to combat baby brain-rot, I bought a Kindle to continue my reading through many many exhausting hours those first few months. Totally worth it, an investment in my sanity.
This post got me remembering my personal experience with Adam Smith. Adam Smith has really gotten a bum rap. He is generally known for inventing the concept of the invisible hand, which is the idea that, as long as each person is working as hard as they can to personally profit from their labor, the overall economy will benefit from that self-interest. However, it’s often used is as an excuse for why regulations are unnecessary, because somehow, the feeling goes, the invisible hand is all we need. To tell you the truth, I don’t even remember seeing that in his book. Maybe it was there, and maybe I was getting barfed on during that page, but he definitely didn’t focus on it. He had other fascinating points though which he did reiterate.
Here’s why Wealth of Nations is so amazing. First, Smith really is incredibly good at explaining how markets work and, considering that he was inventing a field as he was writing, did so extremely well (although at times the book can be a bit repetitive, probably because he never invented notation- he just rewrote out entire phrase whenever he wanted to refer to an idea). The most basic goal of the book is to explain that it makes more sense to trade between countries so that things that are relatively cheaper to make or produce in Country A can be traded for things that are easier for Country B to make, and to generalize that to “between towns” or “between people”.
The examples he uses are really interesting, and include various layered considerations such as whether the goods are easily stored. For example, he maintains that cotton and wools should absolutely have free trade, since there is a clear advantage to having the appropriate climate for the growth of the plants, as well as the long storage. By contrast, he talks about the price of meat in England versus Argentina, being non-storable, and mentions that the price of a cow in Argentina is equal to the tip you need to give a village boy to go catch a cow (I’m paraphrasing because it was almost three years ago).
Another fascinating aspect of the book is that, since he wrote it in the 1770’s, economic conditions were really different, and he talks at length of the peasant classes in various countries. One of the most striking descriptions comes when he describes how much healthier the Irish peasants were compared to the Scottish peasants, because they ate potatoes, whereas the Scots ate oatmeal. It took me a few minutes to realize that he meant, that they only ate oatmeal. And he was saying that you could tell, by the way the 20 year olds still had teeth in Ireland, how much better a staple potatoes are than oatmeal.
He also talks about the various economies of South America and Europe and it sounds like they were doing better than Great Britain, especially Holland, which was a huge trading country back then. It’s fascinating just to understand, at the level of the average person, the peasants and the merchants, how incredibly different the world was then, something you don’t get as good a look at reading history books (at least the history books I’ve read).
Adam Smith was certainly pro-business, in the sense that he wanted a functioning and efficient system to work for all of the people in the world. However, he was well aware of the natural tendencies of people in power to abuse that power. He speaks at length against monopolies, which he thinks are a natural tendency, and claims that regulations to prevent such things are absolutely necessary.
He also talks at length about currencies and bank notes and the concept of borrowing money to be paid later. He is a proponent of usury laws- he doesn’t think it’s fair to entrap people into debt that they can’t repay (and back then I believe the consequences for unpaid debt were pretty severe). He also goes into incredible detail in describing the way Scotland went through a credit crisis, caused by a lending bubble, where people were cycling through various banks with different loans, borrowing more money to repay other debts, and which spiraled into a huge mess which caused the banking system to collapse. The Bank of England itself defaulted as well in one of his other historical accounts of lending bubbles.
One really interesting point he made about the credit crises he talks about is that, in those days, if you had money, which were called bank notes, then if you wanted to use them in another country you’d have to exchange them for gold when you left the country, and then you’d have to exchange the gold back into bank notes when you entered the next country. He claims that this system actually limited the scope of the credit crisis from going beyond the shores of Scotland; he used a kind of conservation of money argument, wherein he considered promised money, i.e. bank notes, to be only probabilistically worth something . Of course there are many parallels to be made to our current credit crisis, but that part about containing the crisis inside a country really makes me think about how much China has lent to the United States.
Adam Smith had one huge blind spot, which was the way he talked about slaves. It was a long time ago and times were different but it’s really hard to read those passages where he talks condescendingly about how naturally lazy slaves are, although he also mentions how little motivation they have. It’s totally brutal, but then again if you read the 1911 Encyclopedia Britannica you will find much the same kind of thing and worse.
Why should you care about statistical modeling?
One of the major goals of this blog is to let people know how statistical modeling works. My plan is to explain as much as I can in simple plain English, with the least amount of confusion, and the maximum amount of elucidation at every possible level, so every reader can take at least a basic understanding away.
Why? What’s so important about you knowing about what nerds do?
Well, there are different answers. First, you may be interested in it from a purely cerebral perspective – you may yourself be a nerd or a potential nerd. Since it is interesting, and since there will be I suspect many more job openings coming soon that use this stuff, there’s nothing wrong with getting technical; it may come in handy.
But I would argue that even if it’s not intellectually stimulating for you, you should know at least the basics of this stuff, kind of like how we should all know how our government is run and how to conserve energy; kind of a modern civic duty, if you will.
Civic duty? Whaaa?
Here’s why. There’s an incredible amount of data out there, more than every before, and certainly more than when I was growing up. I mean, sure, we always kept track of our GDP and the stock market, that’s old school data collection. And marketers and politicians have always experimented with different ads and campaigns and kept track of what does and what doesn’t work. That’s all data too. But the sheer volume of data that we are now collecting about people and behaviors is positively stunning. Just think of it as a huge and exponentially growing data vat.
And with that data comes data analysis. This is a young field. Even though I encourage every nerd out there to consider becoming a data scientist, I know that if a huge number of them agreed to it today, there wouldn’t be enough jobs out there for everyone. Even so, there will be, and very soon. Each CEO of each internet startup should be seriously considering hiring a data scientist, if they don’t have one already. The power in data mining is immense and it’s only growing. And as I said, the field is young but it’s growing in sophistication rapidly, for good and for evil.
And that gets me to the evil part, and with it the civic duty part.
I claim two things. First, that statistical modeling can and does get out of hand, which I define as when it starts controlling things in a way that is not intended or understood by the people who built the model (or who use the model, or whose lives are affected by the model). And second, that by staying informed about what models are, what they aren’t, what limits they have and what boundaries need to be enforced, we can, as a society, live in a place which is still data-intensive but reasonable.
To give evidence to my first claim, I point you to the credit crisis. In fact finance is a field which is not that different from others like politics and marketing, except that it is years ahead in terms of data analysis. It was and still is the most data-driven, sophisticated place where models rule and the people typically stand back passively and watch (and wait for the money to be transferred to their bank accounts). To be sure, it’s not the fault of the models. In fact I firmly believe that nobody in the mortgage industry, for example, really believed that the various tranches of the mortgage backed securities were in fact risk-free; they knew they were just getting rid of the risk with a hefty reward and they left it at that. And yet, the models were run, and their numbers were quoted, and people relied on them in an abstract way at the very least, and defended their AAA ratings because that’s what the models said. It was a very good example of models being misapplied in situations that weren’t intended or appropriate. The result, as we know, was and still is an economic breakdown when the underlying numbers were revealed to be far far different than the models had predicted.
Another example, which I plan to write more about, is the value-added models being used to evaluate school teachers. In some sense this example is actually more scary than the example of modeling in finance, in that in this case, we are actually talking about people being fired based on a model that nobody really understands. Lives are ruined and schools are closed based on the output of an opaque process which even the model’s creators do not really comprehend (I have seen a technical white paper of one of the currently used value-added models, and it’s my opinion that the writer did not really understand modeling or at best tried not to explain it if he did).
In summary, we are already seeing how statistical modeling can and has affected all of us. And it’s only going to get more omnipresent. Sometimes it’s actually really nice, like when I go to Pandora.com and learn about new bands besides Bright Eyes (is there really any band besides Bright Eyes?!). I’m not trying to stop cool types of modeling! I’m just saying, we wouldn’t let a model tell us what to name our kids, or when to have them. We just like models to suggest cool new songs we’d like.
Actually, it’s a fun thought experiment to imagine what kind of things will be modeled in the future. Will we have models for how much insurance you need to pay based on your DNA? Will there be modeling of how long you will live? How much joy you give to the people around you? Will we model your worth? Will other people model those things about you?
I’d like to take a pause just for a moment to mention a philosophical point about what models do. They make best guesses. They don’t know anything for sure. In finance, a successful model is a model that makes the right bet 51% of the time. In data science we want to find out who is twice as likely to click a button- but that subpopulation is still very unlikely to click! In other words, in terms of money, weak correlations and likelihoods pay off. But that doesn’t mean they should decide peoples’ fates.
My appeal is this: we need to educate ourselves on how the models around us work so we can spot one that’s a runaway model. We need to assert our right to have power over the models rather than the other way around. And to do that we need to understand how to create them and how to control them. And when we do, we should also demand that any model which does affect us needs to be explained to us in terms we can understand as educated people.
Some R code and a data mining book
I’m very pleased to add some R code which does essentially the same thing as my python code for this post, which was about using Bayesian inference to thing about women on boards of directors of S&P companies, and for this post, which was about measuring historical volatility for the S&P index. I have added the code to those respective posts. Hopefully the code will be useful for some of you to start practicing manipulating visualizing data in the two languages.
Thanks very much to Daniel Krasner for providing the R code!
Also, I wanted to mention a really good book I’m reading about data mining, namely “Data Analysis with Open Source Tools,” by Phillipp Janert, published by O’Reilly. He wrote it without assuming much mathematics, but in a sophisticated manner. In other words, for people who are mathematicians, the lack of explanation of the math will be fine, but the good news is he doesn’t dumb down the craft of modeling itself. And I like his approach, which is to never complicate stuff with fancy methods and tools unless you have a very clear grasp on what it will mean and why it’s going to improve the situation. In the end this is very similar to the book I would have imagined writing on data analysis, so I’m kind of annoyed that it’s already written and so good.
Speaking of O’Reilly, I’ll be at their “Strata: Making Data Work” conference next month here in New York, who’s going to meet me there? It looks pretty great, and will be a great chance to meet other people who are as in love with sexy data as I am.
How do you disagree?
I remember when I was considering moving to New York from Boston, in late 2004. I came to give a number theory seminar at the CUNY Graduate Center, and afterwards we had a very nice dinner and discussion. Bush had just won re-election, and being typical left-wing academics, we were all disappointed by the news. The most startling aspect of that conversation to me was how often the word “crazy” or “stupid” was used to describe this result. In other words, it seemed like the only way we could come to terms with how half the country had voted for Bush was to describe them as feeble-minded one way or the other.
Gary Gutting wrote a wonderful Opinionator article in today’s New York Times which addresses this issue. It talks about the difference between logical argument and rational thought. He first promotes the idea that we each carry around a developed “picture” of the world:
Conservatives, for example, see business as primarily a source of social and economic good, achieved by the market mechanism of seeking to maximize profit. They therefore think government’s primary duty regarding businesses is to see that they are free to pursue their goal of maximizing profit. Liberals, on the other hand, think that the effort to maximize profit threatens at least as much as it contributes to our societies’ well-being. They therefore think that government’s primary duty regarding businesses is to protect citizens against business malpractice.
He then goes on to say that it’s not irrational to have a picture of the world in mind- we all do it, and it’s an important if not essential way to develop moral, political, and religious views. Moreover, we reasonably view other peoples’ opinions in the context of our pictures, looking naturally for evidence that ours is right.
But what does qualify as irrational is when we stick to our picture in light of really good evidence against its consistency:
But although accepting one of these rival pictures is not irrational, inflexible adherence to it can be. Neither picture would be viable without an exception-clause that acknowledges a certain validity to the rival picture. When an issue about regulation comes up, it’s entirely appropriate (and rational) for liberals and conservatives to begin with an inclination to the response generally favored by their picture. But both sides need to attend to the specific facts of the situation at hand and take seriously the possibility that these facts give reason for invoking the exception-clause in their picture. (For example: The risk from that nuclear plant is too big to take for the sake of free market principles, or the severity of our unemployment makes it worthwhile to exempt small businesses from some record-keeping regulations.) When liberals or conservatives become incapable of thinking this way, their positions become irrational.
I’d like to go one step further (because I agree with everything he said) and ask, what can we do to encourage ourselves and the people we disagree with to have this exception-clause out and ready to use?
It seems to me that when you approach a disagreement armed with facts and arguments to prove your point, you may as well concede defeat before you begin – you won’t “win” an argument that way, at least if it’s a deep argument, even if you can leave it feeling like you made the cleverer points, because you will not have persuaded anyone to change their mind. On the other hand, if you approach disagreement genuinely wondering why the other person feels and thinks the way they do, it becomes much easier to hone in on the basic cause for conflict, and for each person in the discussion to take out their exception-clause and listen to logical argument. In fact I don’t think logical argument can be useful until this point of readiness has been reached. I will call this approach, where you are each mutually assured of the exception-clause readiness before delving into logical argument, as “disagreeing well”.
For example, if I had the time, it would be fascinating to get to know sufficiently many people who voted for Bush in 2004 to be not at all surprised that he won the election. It’s a sad fact about the insularity of my life that I don’t know enough people like that.
More generally, I think a key element of developing your ability to disagree well is to expose yourself to lots of opinions. I am glad to have done a few really different jobs – loading trucks for Fair Foods, barista at Coffee Connection, secretary at a corrupt computer hardware store, student, teacher, quant, professor, data scientist – and met enough people of different classes and backgrounds that I feel relatively exposed to the world- but only the world of the Northeast United States, which is primarily composed of Democrats (although my excursions into the Bluegrass community may be the exception to that rule).
Here’s the irony of disagreeing well: you end up not actually believing your own opinion nearly as much as you thought to begin with. That’s probably why it’s hard to do, because it’s scary to put your belief on the line in an attempt to understand someone else’s viewpoint better. It’s way more work, and it’s for the most part a relationship-building event, with the logical discussion coming in after a long time and sporadically. In particular you can’t plan it and you won’t know how long it will take or even if it will work. I think, though, that to have the most interesting and provocative discussions, we need to do it anyway, even though for the most part you end up more confused than convinced, or convincing.
What about you? How do you disagree well? How do you take out your exception clause and how do you convince other people to do the same?
Cool example of Bayesian methods applied to education
My friend Matt DeLand teamed up recently with Jared Chung to enter a data mining hacking contest sponsored by Donors Choose, which is a well-known online charity connecting low-income classrooms across the country to donors who get to choose which projects to support.
Their goal was to figure out how many of the thousands of projects up for funding were directly related to career preparation, and they performed a nifty Bayesian analysis to do it. Turns out it’s less than 1%!
Here’s their report. It’s really well explained in the 5-page pdf, if you have a few minutes.
Speaking of Donors Choose, it was featured at a HackNY Summer Fellows event I went to last week. The Summer Fellows is essentially like the math camp I taught at for high school students except it’s a computer camp for college students – same level of nerdy loveliness though. The event was a showcase for the fantastically nerdy student hackers, and there were some very impressive exhibits.
The hack involving Donors Choose shows a movie of how the donations are being given from some location to the classroom that’s benefitting on a big map of the country, and shown quickly from 2005 or so really exhibits how quickly the concept grew. It’s not unlike this visualization of the history of the world through the lens of Wikipedia.
Why didn’t anybody invite me!?
There was an attempt yesterday morning to increase transparency on Wall St.
What kind of math nerd job should you have?
Say you’re a math nerd, finishing your Ph.D. or a post-doc, and you’re wondering whether academics is really the place for you. Well I’ve got some advice for you! Actually I will have some advice for you, after you’ve answered a few questions. It’s all about fit. Since I know them best, I will center my questions and my advice around academic math vs. hedge fund quant vs. data scientist at a startup.
By the way, this is the advice I find myself telling people when they ask. It’s supposed to be taken over a beer and with lots of tongue in cheek.
1) What are your vices?
It turns out that the vices of the three jobs we are considering are practically disjoint! If you care about a good fit for your vices, then please pay attention.
NOTE: I am not saying that everyone in these fields has all of these vices! Far from it! It’s more like, if one or more of these vices drives you nuts, then you may get frustrated when you encounter them in these fields.
In academics, the major vices are laziness, envy, and arrogance. It’s perhaps true that laziness (at least outside of research) is typically not rewarded until after tenure, but at that point it’s pretty much expected, unless you want to be the fool who spends all of his(her) time writing recommendation letters and actually advising undergraduates. Envy is, of course, a huge deal in academics, because the only actual feedback is in the form of adulating rumor. Finally, arrogance in academics is kind of too obvious to explain.
At a hedge fund, the major vices are greed, covetousness, and arrogance. The number one source of feedback is pay, after all, so it’s all about how much you got (and how much your officemate got). Plus the isolation even inside your own office can lead to the feeling that you know more and more interesting, valuable, things than anyone else, thus the arrogance.
Finally, at a startup, the major vices are vanity, impatience, and arrogance. People really care about their image- maybe because they are ready to jump ship and land a better job as soon as they start to smell something bad. Plus it’s pretty easy in startups as well to live inside a bubble of self-importance and coolness and buzz. Thus the arrogance. On the flip side of vanity, startups are definitely the sexiest of the three, and the best source by far for good karaoke singers.
Okay it turns out they all have arrogance. Maybe that’s just a property of any job category.
2) What do you care about?
Do you care about titles? Don’t work at a startup.
Do you care about stability? Don’t work at a startup. Actually you might think I’d say don’t work at a hedge fund either, but I’ve found that hedge funds are surprisingly stable, and are full of people who are surprisingly risk averse. Maybe small hedge funds are unstable.
Do you care about feedback? Don’t work in academics.
Do you care about publishing? Don’t work outside academics (it’s sometimes possible to publish outside of academics but it’s not always possible and it’s not always easy).
Do you care about making lots of money? Don’t work in academics. In a startup you make a medium amount of money but there are stock options which may pan out someday, so it’s kind of in between academics and Wall St.
Do you care about being able to decide what you’re working on? Definitely stay in academics.
Do you care about making the world a better place? I’m still working on that one. There really should be a way of doing that if you’re a math nerd. It’s probably not Wall Street.
3) What do you not care about?
If you just like math, and don’t care exactly what kind of math you’re doing, then any of these choices can be really interesting and challenging.
If you don’t mind super competitive and quasi-ethical atmospheres, then you may really enjoy hedge fund quant work- the modeling is really interesting, the pay is good, and you are part of the world of finance and economics, which leaks into politics as well and is absolutely fascinating.
If you don’t mind getting nearly no vacation days and yet feeling like your job may blow up any minute, you may like working at a startup. The people there are real risk lovers, care about their quality of life (at least at the office!), and know how to throw a great party.
If you don’t mind being relatively isolated mathematically, and have enormous internal motivation and drive, then academics is a pretty awesome job, and teaching is really fun and rewarding. Also academic jobs have lots of flexibility as well as cool things like sabbaticals.
4) What about for women who want kids?
Let’s face it, the tenure clock couldn’t have been set up worse for women who want children. And startups have terrible vacation policies and child-care policies as well; it’s just the nature of living on a Venture Capitalist’s shoestring. So actually I’d say the best place to balance work and life issues is at an established hedge fund or bank, where the maternity policies are good; this is assuming though that your personality otherwise fits well with a Wall St. job. Actually many of the women I’ve met who have left academics for government research jobs (like at NASA or the NSA) are very happy as well.
Three strikes against the mortgage industry
There’s a great example here of mortgage lenders lying through their teeth with statistics. Felix Salmon uncovers a ridiculous attempt to make loans look safe by cutting up the pile of mortgages in a tricky way- sound familiar at all?
And there’s a great article here about why they are lying. Namely, there is proposed legislation that would require the banks to keep 5% of the packaged mortgages on their books.
And finally here’s a great description of why they should know better. A breakdown of what banks are currently doing to avoid marking down their mortgage book.
Historical volatility on the S&P index
In a previous post I described the way people in finance often compute historical volatility, in order to try to anticipate future moves in a single stock. I’d like to give a couple of big caveats to this method as well as a worked example, namely on daily returns of the S&P index, with the accompanying python code. I will use these results in a future post I’m planning about errorbars and how people abuse and misuse them.
Two important characteristics of returns
First, market returns in general have fat-tailed distributions; things can seem “quiet” for long stretches of time (longer than any lookback window), during which the sample volatility is a possibly severe underestimate of the “true” standard of deviation of the underlying distribution (if that even makes sense – for the sake of this discussion let’s assume it does). Then when a fat-tailed event occurs, the sample volatility typically spikes to being an overestimate of the standard of deviation for that distribution.
Second, in the markets, there is clustering of volatility- another way of saying this is that volatility itself is rather auto-correlated, so even if we can’t predict the direction of the return, we can still estimate the size of the return. This is particularly true right after a shock, and there are time series models like ARCH and its cousins that model this phenomenon; they in fact allow you to model an overall auto-correlated volatility, which can be thought of as scaling for returns, and allows you to then approximate the normalized returns (returns divided by current volatility) as independent, although still not normal (because they are still fat-tailed even after removing the clustered volatility effect). See below for examples of normalized daily S&P returns with various decays.
Example: S&P daily returns
I got this data from Yahoo Finance, where they let you download daily S&P closes since 1950 to an excel spreadsheet. I could have used some other instrument class, but the below results would be stronger (especially for things like credit default swamps), not weaker- the S&P, being an index, is already the sum of a bunch of things and tends to be more normal as a result; in other words, the Central Limit Theorem is already taking effect on an intraday basis.
First let’s take a look at the last 3 years of closes, so starting in the summer of 2008:
Next we can look at the log returns for the past 3 years:
Now let’s look at how the historical volatility works out with different decays (decays are numbers less than 1 which you use to downweight old data: see this post for an explanation):
For each choice of the above decays, we can normalize the log returns. to try to remove the “volatility clustering”:
As we see, the long decay doesn’t do a very good job. In fact, here are the histograms, which are far from normal:
Here’s the python code I used to generate these plots from the data (see also R code below):
#!/usr/bin/env python
import csv
from matplotlib.pylab import *
from numpy import *
from math import *
import os
os.chdir(‘/Users/cathyoneil/python/sandp/’)
dataReader = csv.DictReader(open(‘SandP_data.txt’, ‘rU’), delimiter=’,’, quotechar=’|’)
close_list = []
for row in dataReader:
#print row[“Date”], row[“Close”]
close_list.append(float(row[“Close”]))
close_list.reverse()
close_array = array(close_list)
close_log_array = array([log(x) for x in close_list])
log_rets = array(diff(close_log_array))
perc_rets = array([exp(x)-1 for x in log_rets])
figure()
plot(close_array[-780:-1], label = “raw closes”)
title(“S&P closes for the last 3 years”)
legend(loc=2)
#figure()
#plot(log_rets, label = “log returns”)
#legend()
#figure()
#hist(log_rets, 100, label = “log returns”)
#legend()
#figure()
#hist(perc_rets, 100, label = “percentage returns”)
#legend()
#show()
def get_vol(d):
var = 0.0
lam = 0.0
var_list = []
for r in log_rets:
lam = lam*(1.0-1.0/d) + 1
var = (1-1.0/lam)*var + (1.0/lam)*r**2
var_list.append(var)
return [sqrt(x) for x in var_list]
figure()
for d in [10, 30, 100]:
plot(get_vol(d)[-780:-1], label = “decay factor %.2f” %(1-1.0/d))
title(“Volatility in the S&P in the past 3 years with different decay factors”)
legend()
for d in [10, 30, 100]:
figure()
these_vols = get_vol(d)
plot([log_rets[i]/these_vols[i-1] for i in range(len(log_rets) – 780, len(log_rets)-1)], label = “decay %.2f” %(1-1.0/d))
title(“Volatility normalized log returns (last three years)”)
legend()
figure()
plot([log_rets[i] for i in range(len(log_rets) – 780, len(log_rets)-1)], label = “raw log returns”)
title(“Raw log returns (last three years)”)
for d in [10, 30, 100]:
figure()
these_vols = get_vol(d)
normed_rets = [log_rets[i]/these_vols[i-1] for i in range(len(log_rets) – 780, len(log_rets)-1)]
hist(normed_rets, 100,label = “decay %.2f” %(1-1.0/d))
title(“Histogram of volatility normalized log returns (last three years)”)
legend()
Here’s the R code Daniel Krasner kindly wrote for the same plots:
setwd(“/Users/cathyoneil/R”)
dataReader <- read.csv(“SandP_data.txt”, header=T)
close_list <- as.numeric(dataReader$Close)
close_list <- rev(close_list)
close_log_list <- log(close_list)
log_rets <- diff(close_log_list)
perc_rets = exp(log_rets)-1
x11()
plot(close_list[(length(close_list)-779):(length(close_list))], type=’l’, main=”S&P closes for the last 3 years”, col=’blue’)
legend(125, 1300, “raw closes”, cex=0.8, col=”blue”, lty=1)
get_vol <- function(d){
var = 0
lam=0
var_list <- c()
for (r in log_rets){
lam <- lam*(1 – 1/d) + 1
var = (1 – 1/lam)*var + (1/lam)*r^2
var_list <- c(var_list, var)
}
return (sqrt(var_list))
}
L <- (length(close_list))
x11()
plot(get_vol(10)[(L-779):L], type=’l’, main=”Volatility in the S&P in the past 3 years with different decay factors”, col=1)
lines(get_vol(30)[(L-779):L], col=2)
lines(get_vol(100)[(L-779):L], col=3)
legend(550, 0.05, c(“decay factor .90”, “decay factor .97″,”decay factor .99”) , cex=0.8, col=c(1,2,3), lty = 1:3)
x11()
par(mfrow=c(3,1))
plot((log_rets[2:L]/get_vol(10))[(L-779):L], type=’l’, col=1, lty=1, ylab=”)
legend(620, 3, “decay factor .90”, cex=0.6, col=1, lty = 1)
plot((log_rets[2:L]/get_vol(30))[(L-779):L], type=’l’, col=2, lty =2, ylab=”)
legend(620, 3, “decay factor .97”, cex=0.6, col=2, lty = 2)
plot((log_rets[2:L]/get_vol(100))[(L-779):L], type=’l’, col=3, lty =3, ylab=”)
legend(620, 3, “decay factor .99”, cex=0.6, col=3, lty = 3)
x11()
plot(log_rets[(L-779):L], type=’l’, main = “raw log returns”, col=”blue”, ylab=”)
par(mfrow=c(3,1))
hist((log_rets[2:L]/get_vol(10))[(L-779):L], breaks=200, col=1, lty=1, ylab=”, xlab=”, main=”)
legend(2, 15, “decay factor .90”, cex=.8, col=1, lty = 1)
hist((log_rets[2:L]/get_vol(30))[(L-779):L], breaks=200, col=2, lty =2, ylab=”, xlab=”, main=”)
legend(2, 40, “decay factor .97”, cex=0.8, col=2, lty = 2)
hist((log_rets[2:L]/get_vol(100))[(L-779):L], breaks=200, col=3, lty =3, ylab=”, xlab=”, main=”)
legend(3, 50, “decay factor .99”, cex=0.8, col=3, lty = 3)
Is too big to fail a good thing?
I read this blog post a couple of days and it really got me thinking. This guy John Hempton from Australia is advocating the too big to fail model- in fact he things we should merge more big banks together (Citigroup and Wells Fargo) because we haven’t gone far enough!
His overall thesis is that competition in finance increases as a function of how many banks there are out there and is a bad thing for stockholders and for society, because it makes people desperate for profit, and in particular people increase their risk profiles in pursuit of profit and they blow up:
What I am advocating is – that as a matter of policy – you should deliberately give up competition in financial services – and that you should do this by hide-bound regulation and by deliberately inducing financial service firms to merge to create stronger, larger and (most importantly) more anti-competitive entities.
He acknowledges that the remaining banks will be hugely profitable, and perhaps also extremely lazy, but claims this is a good thing: we would, as a culture, essentially be paying a fee for stability. It’s something we do all the time in some sense, when we buy insurance. Insurance is a fee we pay so that disruptions and small disasters in our lives don’t completely wipe us out. So perhaps, as a culture, this would be a price worth paying?
The biggest evidence he has that this setup works well is that it works in Australia- they have four huge incompetent yet profitable banks there, and they don’t blow up. People who work there are sitting pretty, I guess, because they really are just living in a money press. There is no financial innovation because there’s no competition.
I guess I have a few different reactions to this scenario. First, it’s kind of an interesting twist on the too-big-to-fail debate, in that it’s combined with the idea I already talked about here of having a system of banks that are utilities. John is saying that, really, we don’t need to make that official, that as soon as banks are this huge, we are already done, they are essentially going to act like utilities. This is super interesting to me, but I’m not convinced it’s a necessary or even natural result of huge banks.
Second, I don’t buy that what happened in Australia will happen here- perhaps Australia squelched financial innovation through regulations and the existing boring system, but maybe the people who would have been financial innovators all just moved to the U.S. and became innovators here (there are plenty of examples of that!). In other words Australia may have made it just a bit too difficult to be competitive relative to what else is out there- if everyone tried to be that repressive to financial innovation, we may see people moving back into Australia’s financial waters (like sharks).
Third, I think what John is talking about is an example of a general phenomenon, namely that, in the limit as regulations go to infinity, there is only one bank left standing. This is because every additional regulation requires a lawyer to go over the requirements and a compliance person to make sure the rules are being followed continuously. So the more regulation, the more it behooves banks to merge so that they can share those lawyers and compliance officers to save costs. In the end the regulations have defined the environment to such an extent that there’s only one bank that can possibly follow all the rules, and knows how to because of historical reasons. And that one, last bank may as well be a government institution, albeit with better pay, especially for its managers.
But we don’t have that kind of regulatory environment, and hedge funds are alive and well. They have to follow some rules, it’s absolutely true, but it’s still possible to start a smallish hedge fund without a million lawyers.
I guess what I’m concluding is that if we had formed our very few, very huge banks because of a stifling regulatory environment, then maybe we would have an environment that is sufficiently anti-competitive to think that our banks would serve us as slightly overpaid utilities. However, that’s not why we have them – it was because of the credit crisis, and the rules and regulations haven’t changed that much since then.
At the same time, I don’t totally disagree that huge banks do become anti-competitive, just by dint of how long it takes them to make decisions and do things. But I’m not sure anti-competitive is the same thing as low-risk.
Elizabeth Warren: Moses and the Promised Land
This is a guest post by FogOfWar
In Biblical style, Elizabeth Warren (EW) was not nominated to head the CFPB (Consumer Financial Protection Bureau). Having spearheaded the movement to create the institution, pushed to make it part of the otherwise-generally-useless* Dodd Frank “Financial Reform” Bill, and spent the better part of the last two years staffing the actual CFPB and moving it into gear, she has now been deemed too controversial by what passes for a President these days.
One of my favorite EW quotes: “My first choice is a strong consumer agency. My second choice is no agency at all and plenty of blood and teeth left on the floor.” This still remains to be seen, as opposition to the CPFB (and filibuster threats to any appointment to head the Bureau) remains in the face of nominee Richard Cordray. In fact, if one were inclined to be an Obama apologist (I gave up apologizing for Obama right about here), one might view the Warren-Cordray switch as a potentially brilliant tactical maneuver, with the emphasis on “potentially”. If the opposition to the CPFB took its persona in EW, then sidestepping her personally to get the agency up and running would be worthwhile, particularly as Cordray seems at least as assertively pro-consumer as EW (a bank lobbyist described him as “Elizabeth Warren without the charm”).
Barney Frank believes gender bias played a role. Maybe yes, maybe no and the Cordray confirmation will give some evidence to that question. I suspect the Republican opposition isn’t stupid and knows that Cordray will run a good agency. If that’s right then passing over EW doesn’t really serve any purpose.
Hard to tell what a public figure is really like, but my sense is EW doesn’t have any ego attached to running the agency personally. And what she does next is really up to her, I mean who really cares what we think she should do?
Wait—this is a blog! Our Raison d’être is practically making suggestions that no one will listen to, so let’s go…
1. Run for Congress
The biggest idea floated around. Yves Smith thinks it’s a terrible idea. I’m not entirely convinced—there are many ways to make a difference in this world, and being one minority member of a large and powerful body, and thus moving the body incrementally in the right direction can be a very good thing.
Two questions though: can she win (a few early stage polls seemed to indicate no, but do early stage polls really have much predictive value on final election results? Cathy? Fivethirtyeight?), and on which party platform would she run (I vote for running as an Independent)? Any thoughts from the ground from our MA-registered voters?
2. The “Al Gore” option
EW could continue to advocate, lecture and write outside of political office. She’s good television and would be free to speak without the gag order of elected office. Definitely something to be said for this option. Just realized pulling links for this post that EW was the person from the movie “Maxed Out”. Part of me thinks “damn that was effective and she should do more of that because it was so effective” and part of me thinks “wait, that movie came out in 2006 and no one listened and no one will listen”, and then the other part goes “but it can happen—you’ve actually seen social perceptions change in the wake of Al Gore (and yes, lots and lots of other people, but sparks do matter) with real and deep impacts.”
3. The “Colin Powell” option
Y’now, being in the public light kinda sucks ass. Colin Powell passed up a run for President, and largely retired to private life, and doesn’t seem to have any complaints about it. One legitimate option is to say “I did my part, you guys fight the good fight & I’m going to hang out with my grandkids on the beach.”
Any other suggestions?
*-Paul Volker deserves a parallel post of equal length for pushing the Volker Rule through this legislation and similarly receiving the thanks of being sidelined by the TBTF bank-capital-must-increase-even-if-the-peasants-have-to-eat-cake crowd.
Quit your job and become a data miner!?
Today my friend sent me this link, which is a pretty interesting and inspiring video of a talk from a guy from Google named Steve Yegge talking at an O’Reilly conference about how he’s sick of working on uninspiring projects involving social media and cat pictures, and wants to devote himself (and wants you to devote yourself) to more important questions about the nature of human existence. And he things the way to go about this is to become a data miner. I dig it! Of course he’s preaching to the choir at that conference. I wonder what other people will make of his appeal. Can one nerd change an entire culture of endless cat pic collections?
And lest you think that data mining is the answer to everything, here’s an article about how much data mining (in the form of “Value-added modeling”) can screw up other peoples’ lives when it’s misdirected. It’s written by John Ewing, who is the fabulous president of MfA, an organization that trains and mentors excellent college math majors to become effective math teachers in the New York Public School system and beyond- the “beyond” part is partly due to the crazy state of the budgets for new teachers here in NYC- we now have access to these wonderful MfA graduates but have hiring freezes so we can’t hire them. Also, my good friend Japheth Wood, a.k.a. the Math Wizard, is one of the MfA mentors.
I’m planning to post more soon on how crappy the value-added modeling (VAM) system is and how’s it’s a perfect example of mathematics being used to make things seem magical and therefore inaccessible, the exact opposite of what should be going on.
The Bad Food Tax
There’s an interesting op-ed article in today’s New York Times. The author, Mark Bittman, is proposing that we tax bad foods to the point where people will naturally select healthy food because they will be subsidized and cheap.
He has lots of statistics to back him up, and if you’re someone like me who reads this kind of thing widely, nothing surprised me. Of course Americans eat crappy food and it’s terrible for our bodies. We know that, it’s old news.
And we all want to know how to fix this- clearly education about nutrition isn’t doing the trick by itself. And I’m the first person who would love to use quantitative methods to solve a really important, big problem. Moreover, if we start to get rid of the evil farm subsidies that are currently creating a ridiculous market for corn sugar (a major reason we have some much soda on the shelves at such low prices to begin with) as well as screwing up the farmers in Africa and other places, that will be a good thing.
Unfortunately, I really think his tax plan stinks. The main problem is something he actually brings up and dismisses- namely:
Some advocates for the poor say taxes like these are unfair because low-income people pay a higher percentage of their income for food and would find it more difficult to buy soda or junk. But since poor people suffer disproportionately from the cost of high-quality, fresh foods, subsidizing those foods would be particularly beneficial to them.
Yes they would, if they could actually buy them in their neighborhood! If he has the idea that the reason poor people buy crappy food is because they go into their neighborhood grocery store with a museum-like display of fresh fruits and vegetables, bypass those foods (because they are too expensive) to go straight to the back and find junk, then I guess his plan would make sense. Unfortunately the truth is, there is no fresh fruit at most of the food stores in poor urban areas – they are typically small and carry long-lasting packaged goods and groceries, from canned evaporated milk to diapers, and don’t have extra space. Moreover, I don’t think a pure price comparison is going to convince them to carry fruit, because it’s not just the higher prices that makes bodegas carry no fruit- it’s also the convenience of packages that don’t go bad. In fact it’s an entirely different business model, which is unfortunately a pretty tough nut to crack, but is essential in this discussion.
In other words, the result of this tax plan would be, for poor people, even higher prices for crappy food, not access to fresh cheap food. Unless the plan has worked out a system for how to get fresh fruit into poor areas, it really is missing the very audience it wishes to target.
mathbabe 