Do higher taxes kill jobs?
Being a mathematician, I find myself forced to consider statements like “higher taxes kill jobs” as statements of theorems with missing stated assumptions. How could you fill in the assumptions and prove this theorem?
First I think about extreme cases- sometimes extreme situations need fewer assumptions, they kind of spill out as obvious. So here’s one, the tax rate is at 80%, what would happen if we raised taxes? My first reaction is, 80%!? That must mean you have way too much government and regulation and for those reasons businesses are probably already quite pinned down and don’t have lots of freedom- don’t tax them more, that will make their good ideas (if they have them) all the more suffocated. Just think of the paperwork you’d need to go through in a society that government-heavy, to hire someone.
What’s another extreme case? How about taxes are super low, more like fees for doing business. Then no, I don’t think raising them a moderate amount would kill jobs at all, in fact it may introduce enough government to make things less wild west and safer for businesses to operate.
So in other words at some level I buy the anti-regulation anti-government angle. I don’t want super duper high taxes because I think it encourages too much bureaucracy and that stuff is boring (but some amount of it is necessary to make things safe).
Moreover I’m assuming that governments generally use taxes to protect people from food poisoning and the like, regulate to force companies to play fair, and as social safety nets when things go bad, and that they’re not particularly efficient. Those of course are my assumptions, which anyone can disagree with.
But in terms of proving my theorem, I’m stuck thinking it’s more like, there’s some point in between very low and very high taxes where it gradually becomes true that raising taxes more will indeed start to kill jobs.
How about our situation now? Right now we have pretty low taxes by historical measures, and moreover the known loopholes mean that businesses (especially big ones with fancy lawyers) pay much less than their stated tax rate.
Why, in this case, would a moderate bump in their tax rates kill jobs?
Here’s a possible argument: if higher taxes actually encourage more regulation, then that could be a major problem for smaller businesses, who don’t have the margin for dealing with hiring that many lawyers for compliance issues. Although this article argues that “regulation kills jobs” is an invalid statement in general.
Pet peeve of mine: when you hear conservatives talk about killing jobs, they often frame it in terms of struggling small businesses, often run by a woman. But it’s easy enough to imagine that we introduce taxes and regulation that are easier for small businesses to avoid smothering them. It’s really the huge businesses that we want to see start hiring, and it’s the huge businesses that pay so little taxes.
Here’s another one: if you raise taxes people will spend their cash on taxes instead of hiring people. But wait, that doesn’t apply right now when we have so much frigging cash on hand (and hidden in other countries). In other words, companies are not not hiring people for cash flow reasons, it’s because they don’t see the demand.
In the end I can’t see how to prove or even argue that theorem, assuming today’s conditions. Would love to hear the argument I’m missing.
Back from Strata Jumpstart
So I gave my talk yesterday at the Strata Jumpstart conference, and I’ll be back on Thursday and Friday to attend the Strata Conference conference.
I was delighted to meet a huge number of fun, hopeful, and excited nerds throughout the day. Since my talk was pretty early in the morning, I was able to relax afterwards and just enjoy all the questions and remarks that people wanted to discuss with me.
Some were people with lots of data, looking for data scientists who could analyze it for them, others were working with packs of data scientists (herds? covens?) and were in search of data. It was fun to try to help them find each other, as well as to hear about all the super nerdy and data-driven businesses that are getting off the ground right now. It certainly was an optimistic tone, I didn’t feel like we were in the middle of a double-dip recession for the entire day (well, at least til I got home and looked at the Greek default news).
Conferences like these are excellent; they allow people to get together and learn each others’ languages and the existence of the new tools and techniques in use or in development. They also save people lots of time, make fast connection that would otherwise difficult or impossible, and of course sometimes inspire great new ideas. Too bad they are so expensive!
I also learned that there’s such thing as a “data scientist in residence,” held of course by very few people, which is the equivalent in academic math to having a gig at the Institute for Advanced Study in Princeton. Wow. I still haven’t decided whether I’d want such a cushy job. After all, I think I learn the most when I have reasonable pressure to get stuff done with actual data. On the other hand maybe that much freedom would allow one to do really cool stuff. Dunno.
How do you standardize tests?
I’m reading an interesting book by Douglas Harris about the value-added model movement, called Value-added Measures in Education, available here from Harvard Education Press. Harris goes into a very reasonable critique of how “snapshot” views of students, teachers, and school are a very poor assessment of teacher ability, since they are absolute measurements rather than changes in knowledge. Kind of like comparing the Dow to the S&P and concluding that you should definitely invest in Dow stocks since they are ten times better, it’s all about the return on a test score or an index, not the absolute number, when you are trying to gauge learning or profit.
His goal of the book is to explain how value-added models work, how they measure learning, how the take into account things like poverty level and other circumstances beyond the control of the school or the teachers, and other such factors. In his introduction he also promises not to be unreasonable about applying the results of these tests beyond where it makes sense. He certainly seems to be a smart guy; smart enough to know about errors and the problems with badly set up incentives – he uses the financial crisis as a model of how not to do it. I’m hopeful!
Here’s what I am interested in talking about today, which is how the “standardized” gets into standardized testing, because already at this point the mathematical modeling is pretty tricky (and involves lots of choices). There are many ways a test is ultimately standardized, assuming for simplicity that it’s a national test given at many grade levels yearly (pretend it’s an SAT that every grade takes):
- the test is normalized for being harder or easier than it was last year, for each grade’s test separately, and sometimes per question as well,
- the grading is normalized so that a student who learns exactly as much “as is expected” gets the same grade from year to year, and
- the grading is further normalized so that a student who gets 10 more points than expected in 3rd grade is doing as well as if she got 10 extra points in 4th grade.
One way of accomplishing all of the above would be to draw a histogram of raw results per year and per grade and normalize that distribution of raw scores by some standard mean and standard deviation, just as you would make a normal distribution standard, i.e. mean 0 and standard deviation 1. In fact, go ahead and demean it and divide by the standard deviation. That’s the first thing I’d do.
But if you actually do that, then you lose lots of the information you are actually trying to glean. Namely, how could you then conclude if students are doing better or worse than last year? I’m sure you’ve seen the recent news that SAT scores have fallen this year from last. I guess my question is, how can they tell? If we do something as simple as what I suggested, then the definition of doing as well “as is expected” is that you did “as well as the average person did”. But clearly this is not what the SAT people do, since they claim people aren’t doing as well as they used to. So how are they standardizing their test?
It isn’t really explained here or here, but there are clues. Namely, if you give 3rd and 4th graders some of the same questions on a given year, then you can infer how much better 4th graders do on those questions than 3rd graders do, and you can use that as a proxy for how to scale between grades (assuming that those questions represent the general questions well). Next, since you can’t repeat questions (at least questions that count towards the score) between years, because the stakes are too high and people would cheat, you can instead have ungraded sections that have repeated questions which give you a standard against which to compare between years. In fact the SAT does have ungraded sections, and so did the GREs as I recall, and my guess is this is why.
That brings up the question, do all standardized tests have ungraded sections? Is there some other clever way to get around this problem? Also in my mind, how well does standardization work, and what is a way to test it?
What the hell is going on in Europe?
This week has been particularly confusing when it comes to the European debt crisis. It’s complicated enough to think about the various countries, with their various current debt problems, future debt problems, and austerity plans, not to mention how they typically interact at the political level versus how the average citizen is affected by it all. But this week we’ve seen weird and coordinated intervention by a bunch of central banks to address a so-called “liquidity crisis”.
What is this all about? Is it actually a credit crisis disguised as a liquidity crisis? Is it just another stealth way to bail out huge banks?
I’m going to take a stab at answering these questions, at the risk of talking out of my ass (and when has that ever stopped me?).
Finance is a big messy system, and it’s hard to know where to begin on the merry-go-round of confusion, but let’s start with European banks since they are the ones in need of funding.
European banks have lots of euros on hand, just as American banks have lots of dollars, because of the actual deposits they hold. However, European banks invest in American things (like businesses) that need them to come up with short term funding denominated in dollars. Similarly American banks invest in Europe, but that’s not really relevant to the discussion yet.
How do European banks get these short term (3 month) loans? Historically they do a large majority of it through money-markets: much of the money people have in banks is funneled to huge vats called money markets, and the fund managers of those vats are very very conservatively trying to make a bit of interest on them. In fact they were burned in the credit crisis, when they famously “broke the buck” on Lehman short-term loans.
Well, guess what, those same American money managers are avoiding European short-term loans right now, because they are super afraid of losing money on them. So that source of funding has dried up. Note that this is a credit problem: the money market managers do not trust the banks to be around in 3 months.
Another source of funding for the European banks’ American investments has been just to use their euros, exchange them to dollars (the currency market is very very large and liquid, especially on this particular exchange), then wait until the term of the short-term financing is over, and then convert the dollars back to euros. What actually happens, in fact, is that they borrow euros (at the going rate of 1%), do the exchange, then financing, and then get their money back in the future.
The guys who work at the European banks and who do this short-term financing aren’t allowed to take on the risk that the exchange rate is going to violently change between now and when the short-term term is over. Therefore they need to hedge the risk, which means they have to have a guarantee that the dollars they get out at the end of the term will be turned into a reasonable number of euros.
This kind of guarantee is called a currency swap, and the market for those is also very large and liquid, but has been less liquid recently because of the one-sidedness of this problem: European banks need short-term dollars but American banks don’t need euros at the same rate at the same maturity. So the end result is that the swaps are very very expensive for European banks.
Let’s put this another way, the way that seems strangest and most confusing: right now the European banks can borrow at 1% in euros but at 4% in dollars (for three month maturity), and more generally the demand for USD seems to be skyrocketing recently from all over the place. Does this mean there’s an arbitrage opportunity somewhere? The swaps market is at 3% so no obvious arbitrage. More likely it means that the markets are expecting the exchange rate to drastically change, or at least they are pricing in the risk of it changing violently in the very near future. (The strangest thing to me is why it hasn’t just changed the spot exchange rate as well.)
By the way, a pet peeve or two I have with people talking about arbitrage: firstly, many people use the term so loosely it means nothing at all, as when they take risk over time (exposing themselves to the possibility of an exchange rate change for example). But even here, I’m misusing the term, since in an arbitrage it’s literally supposed to be a way to make money risk-free, but the whole point of my post is that this is really all about counter-party risk! In other words, there’s no arbitrage opportunity to get into contracts with people where you’d make money except if they go bankrupt tomorrow, when there’s a good chance that will happen.
The bottomline is that although the ECB and the Fed and the other central banks have spun this as a coordinated effort to help out a liquidity squeezed but functional market, it doesn’t pass the smell test. What’s actually happening is that the shoddy accounting and investments of French banks and others is not being trusted by American money market managers who are wise to them.
One more thing: the collateral being asked of the European banks is purportedly of low standard, which is to say the ECB is allowing thing like Greek debt as collateral, which wouldn’t past muster with other institutions (or with U.S. money markets!). In that sense this can be seen as a stealth bailout, although I think not the first one in Europe under that definition. This isn’t going away until they figure out how to deal with the Greek debt problem.
Household debt amnesty?
It’s Saturday morning, which means it’s time to conduct a thoroughly absurd thought experiment just for the sake of argument. Today I want to consider the idea of a widespread household debt amnesty: everyone who owes money on their credit cards and payday loans and also perhaps mortgage will be forgiven their debt (although mortgages would have to be rewritten rather than forgiven). What would happen next?
I was discussing this very question, and David Graeber’s book (still not finished- it’s long!) with a friend of mine, specifically how Graeber cites ancient Sumerian civilization as having periodically enacted household debt amnesties to avoid the collapse of their cities (specifically to avoid the debtors from fleeing the cities to avoid their debt problems).
One thing that I realized in that conversation is that, whereas Graeber mentions that it was historically an amnesty for household debt only, so didn’t involve commercial debt between companies and merchants, what we’ve seen in this country in the past 3 years is something like the opposite of that concept. Our (large financial) companies have been granted special considerations while the people who had made the mistake of entering contracts with them have not. And the so-called mortgage modification process has not been sufficiently widespread yet to really consider it an example of this. There is an excellent article here which makes this point, although not in this context.
The objection my friend had to the idea of enacting such an amnesty was that it would be pouring good money after bad; he’s European so he cited the example of Greece, and how the more money Greece gets the more money they spend, so it’s an impossible situation.
Actually I think this is an appealing analogy to make, but it’s a false one. Greece has a large-scale system in place, and giving them money without changing that system clearly isn’t going to solve any long term problems- it just kicks the can down the road.
However, it’s really different with consumer debt (credit cards etc.). Namely, the “system” that a given consumer enters into is a simple relationship (contract) with the credit card company in question. If the debt is forgiven, then the credit card company doesn’t have any obligation to extend more credit to that person. And in many cases, it wouldn’t.
I think the consequences of a household debt amnesty would be something along these lines:
- People who were previously in debt would have some cash on hand and would be able to spend it on consumer stuff (that they can actually afford with no credit) instead of spending it all on minimum payments to old credit card debt
- Credit card companies, burned from their losses, wouldn’t give them new credit cards, or would change the payment arrangements to make sure they got their money back faster
- Since they have no credit, those people who essentially be living in a cash-dominated society. This may actually be a good thing, because it would force people to budget in real time.
- Eventually people could rebuild a credit score over time if they decided to try credit again
In other words, that’s really not so bad and would get money flowing through the system again, which might help with our current recession.
Of course not everyone would be happy about a household debt amnesty. In particular the people who aren’t debtors would feel pretty burned that they’ve been careful (or lucky) with their money and aren’t getting a free ride. And the credit card companies would have to eat a lot of loss. On the other hand they’re going to eat (and have eaten) a lot of loss already, and the slowness of this process is killing the economy.
Is there a way we could set it up to make this work? Even if we ignore the political obstacles?
Guest Post: What is a family?
By guest blogger rwitte
The social structures commonly talked about when discussing finance and economics include individuals, governments and corporations. However the most social structure is family, since it is the structure that results in the perpetuation of society. I was reminded of this by a recent link that mathbabe posted here. And since she invited me to write a guest post, it inspired me to mouth off about family.
What do you think of when someone uses the word family? I am guessing that you think of the so-called nuclear family consisting of a husband a wife and a variable number of children. For sure their are many variants including one-parent families and gay families, but the ideal is thus. It wasn’t always so. I am a fifty year old male of Ashkenazi Jewish descent. In that community my generation is the first to have prioritise the needs of small nuclear families. My grandmother’s idea of family was a much larger group, extending over several generations, and with a healthy side-order of cousins, aunts etc. My wife is Jaimaican, and her mother’s conception of family is similar to my grandmother’s (except the Jamaican version is even more matriarchal because so many of the fathers are absent).
I don’t know if you have ever seen a family home from a hundred and fifty or more years ago. You will be surprised at the size; there are more rooms for a family, at any level in the social scale. This is because the experiences of my wife and I are not unique, they are just a few generations later than those of the majority. Until relatively recently, as in most ‘primitive’ societies, the family is the extended family, and you will commonly find three or four generations living together.
I think the organisation of society into extended families was a great idea, and that the fragmentation into nuclear families sucks. But before I explain what’s so terrible about them I want to take a paragraph to explain why I think the change occured in the first place. Nuclear families are small and relatively mobile. As industrialization progressed and first transcontinental and then transglobal corporations formed, it suited their purpose to be able to move and resettle employees between different sites in their empires. At the other end of society the pull of the factories was encouraging many to move from rural areas to the big city. This also fragmented extended families; the units that moved were nuclear. As a process in the developed countries, it probably peake in the 1950s or 1960s, but it is still going on now in developing countries such as China.
The original myth of the nuclear family was one in which the male was the breadwinner and married women stayed home to provide full time childcare. This idea, obviously sexually discriminatory, is certainly a myth. It has never been the case that poor couples could support themselves on one person’s wages. Manual labour has never been that well payed. And since the rich typically had access to nannies, only a thin stratum of society has ever organized childcare this way.
Today, in an attempt to paper over the cracks in this story, a new myth has arisen. Namely, the ‘superwoman’ who has it all: career, children, and social life (it only take 28 hours a day eight days a week!). In actual fact this myth is probably even more dangerous than the previous one, because millions of women are now trying to live up to this impossible ideal. When they don’t overachieve these impossibly demanding targets, they feel guilty and inadequate. For some, serious mental health issues can ensue as the buckle under the pressure.
We often complain about the poor quality of life our society offers to our elders. They get stuck in some retirement home quitely out of sight where they can slowly die of boredom. The middle classes must pay for child-care and the exorbitant prices push them towards poverty. Meanwhile poor parents simply cannot afford adequate child care and poor children roam the streets; ‘latch-key kids’ with no adequate supervision between the end of school and the parents’ return from the work. Some of these children get really out-of-control; getting involved with drugs, crime, and street-gangs in some combination. I don’t want to get too carried away with arguments about ‘the youth of today’ because the rose-tinted vision painted nostagia never was, but still I hear the cries, something must be done.
I believe that we can overcome all these social problems by returning to a social organisation based on extended families. The grandparents can look after the children while the able-bodied parents go out to work. This leads to an interesting and fulfilling retirement, in which they can pass on the wisdom that they have accumulated over the years to a willing audience. The children get an educational, loving and supportive home-life which will help them to grow up honest and secure. And the fittest adults can commit 100% to the workforce increasing social productivity.
Of course it may be that the grandparents are still too young to retire and the great-grandparents take on the responsibility. Or maybe a cousin or Aunt who particularly enjoys childcare can set up a sort of family creche. Perhaps all the adults work and coordinate their days off in a rota so that who looks after the children depends on the day of the week. Nobody would be surprised to discover that while all families have much in common, every family is different. The role of the greater society is as to encourage and enable relatives to remain geographically close to each other. It would be up to each particular family to work out how to organise themselves for their own convenience (although there is some evidence to suggest that children brought up by maternal grandparents are more psychologically secure than those brought up by paternal grandparents).
Modern advances in information and communication technology may render a society with less geographical relocation of people possible. You don’t have to be in the same office to work with somebody (hell, you don’t even have to be in the same continent). Instead of workers having to move around the globe, they can stay with their parents and children while their information flows around the internet and other computing networks, more quickly, conveniently and cheaply.
Politics: the good news and the bad news
I love Elizabeth Warren. I think she’s real, she cares about people, she’s smart and tough, and she’s incorruptible. The bizarre news, under these circumstances, is that she’s running for Congress. Is she too good to win in politics? Don’t you have to be a slime ball? Don’t you need to attract and sell out to megacorporations to succeed? We shall see. It could be really great.
And just in case you think I’m being too cynical, please read this. It’s an absolutely stunning, depressing insider’s view of the Republic Party from Mike Lofgren, who retired in June after 28 years as a Congressional staffer, having served 16 years as a professional staff member on the Republican side of both the House and Senate Budget Committees.
The pandas module and the IPython notebook
Last night I attended this Meetup on a cool package that Wes McKinney has been writing (in python and in cython, which I guess is like python but is as fast as c). That guys has been ridiculously prolific in his code, and we can all thank him for it, because pandas looks really useful.
To sum up what he’s done: he’s imported the concept of the R dataframe into python, with SQL query-like capabilities as well, and potentially with some map-reduce functionality, although he hasn’t tested it on huge data. He’s also in the process of adding “statsmodel” functionality to the dataframe context (he calls a dataframe a Series), with more to come soon he’s assured us.
So for example he demonstrated how quickly one could regress various stocks against each other, and if we had a column of dates and months (so actually hierarchical labels of the data), then you could use a “groupby” statement to regress within each month and year. Very cool!
He demonstrated all of this within his IPython Notebook, which seems to demonstrate lots of what I liked when I learned about Elastic-R (though not all, like the cloud computing part of Elastic-R is just awesome), namely the ability to basically send your python session to someone like a website url and to collaborate. Note, I just saw the demo I can’t speak from personal experience, but hopefully I will be able to soon! It’s a cool way to remotely use a powerful machine and not need to worry about your local setup.
What are the chances that this will work?
One of the positive things about working at D.E. Shaw was the discipline shown in determining whether a model had a good chance of working before spending a bunch of time on it. I’ve noticed people could sometimes really use this kind of discipline, both in their data mining projects and in their normal lives (either personal lives or with their jobs).
Some of the relevant modeling questions were asked and quantified:
- How much data do you expect to be able to collect? Can you pool across countries? Is there proxy historical data?
- How much signal do you estimate could be in that data? (Do you even know what the signal is you’re looking for?)
- What is the probability that this will fail? (not good) That it will fail quickly? (good)
- How much time will it take to do the initial phase of the modeling? Subsequent phases?
- What is the scope of the model if it works? International? Daily? Monthly?
- How much money can you expect from a model like this if it works? (takes knowing how other models work)
- How much risk would a model like this impose?
- How similar is this model to other models we already have?
- What are the other models that you’re not doing if you do this one, and how do they compare in overall value?
Even if you can’t answer all of these questions, they’re certainly good to ask. Really we should be asking questions like these about lots of projects we take on in our lives, with smallish tweaks:
- What are the resources I need to do this? Am I really collecting all the resources I need? What are the resources that I can substitute for them?
- How good are my resources? Would better quality resources help this work? Do I even have a well-defined goal?
- What is the probability this will fail? That it will fail quickly?
- How long will I need to work on this before deciding whether it is working? (Here I’d say write down a date and stick to it. People tend to give themselves too much extra time doing stuff that doesn’t seem to work)
- What’s the best case scenario?
- How much am I going to learn from this?
- How much am I going to grow from doing this?
- What are the risks of doing this?
- Have I already done this?
- What am I not doing if I do this?
Monday morning links
First, if you know and love the Statistical Abstract as much as I do, then help save it! Go to this post and follow the instructions to appeal to the census to not discontinue its publication.
Next, if you want evidence that it sucks to be rich, or at least it sucks to be a child of rich people, then read this absolutely miserable article about how rich people control and manipulate their children. Note the entire discussion about “problem children” never discusses the possibility that you are actually a lousy, money-obsessed and withholding parent.
Next, how friggin’ cool is this? Makes me want to visit Mars personally.
And also, how cool is it that the World Bank is opening up its data?
Finally, good article here about Bernanke’s lack of understanding of reality.
Working with Larry Summers (part 3)
Previously I’ve talked about the quant culture of D.E. Shaw as well as the tendencies of people working there. Today I wanted to add a third part about the experience of being “on the inside looking out” during the credit crisis.
I started my quant job in June 2007, which was perfect timing to never actually experience unbridled profit and success; within two months of starting, there was a major disruption in the market which caused enough momentary panic and uncertainty that the Equities group decided to liquidate their holdings. This was a big deal and meant they lost quite a bit of money on transaction costs as well as losing money because other investors were pulling out of similar trades at the same time.
The August 2007 market disruption was referred to internally as “the kerfuffle”. I’ve grown to think that this slightly dismissive term, which connotates more of an awkward misunderstanding than any real underlying problem, was indicative of a larger phenomenon. Namely, there was a sense that nothing really bad was afoot, that the system couldn’t be at risk, and that as long as we kept our trades on balance market neutral, we would be fine, except for possibly bizarre moments of exception. The tone would be something like, if an upper class man went to a restaurant and his credit card was denied- the waiter would return the credit card with almost an apology, assuming that it must have expired or something, that surely it is a mistake more than an exposure of underlying bankruptcy.
This framing of the world around us, as individual exceptional moments, as mysterious, almost amusing singularities in an otherwise smooth manifold, continued throughout the credit crisis (I left in May 2009), with the exception of the days after Lehman collapsed (Lehman was a 20% owner of D.E. Shaw at the time of its collapse, as well as a one of our major brokers).
But Lehman fell kind of late in the game, actually, for those in the industry. In other words there were months and months of disturbing signs, especially in the overnight lending market (where banks lend to each other for just the night or over the weekend) leading up to the Lehman moment. I remember one experience during those times that still baffles me.
It was a company-wide event, an invitation to see Larry Summers, Robert Rubin, and Alan Greenspan chat with each other and with us at the Rainbow Room in Rockefeller Center. It started with a lavish spread, fit for the dignitaries that were visiting, as well as introductory remarks wherein David Shaw described Larry Summer’s appointment as managing director at D.E. Shaw a “promotion” from being President at Harvard (just to be clear, this was a joke – even David Shaw isn’t that arrogant). In incredibly collegial terms, each of the three spoke for some time and reminisced about working together in the Clinton administration. Whatever, that’s not the important part, although it is kind of strange to think about now.
The important part, in retrospect, was later, near the end, when Alan Greenspan started talking about CMO‘s and how worried he was that anybody investing in them was in for a world of hurt. When I had gotten to D.E. Shaw, one of the first presentations I’d ever gone to was by a guy describing how he thought the same thing, and how we had divested ourselves of any such holdings, at least for the high-risk kind. So when Greenspan asserted these warnings, I sensed quite a bit of smugness in the crowd around me. It made me imagine us investors as a bunch of people playing illegal poker in the back of a club, where the smartest ones in the game get told a few minutes before the cops come and they leave out the back (except in this case it wasn’t actually illegal, and it was retired cops- Greenspan left the Fed at the end of 2006).
I wish I could remember when exactly that Rainbow Room event was, because I specifically remember Rubin saying absolutely nothing and looking uncomfortable when Greenspan was going on about CMOs and the danger in their future. Way later, it was revealed that Rubin, who was being paid obscene amounts by Citigroup at the time, claimed not to know about how toxic those mortgage-backed securities were (nor did he claim to know how much Citibank had invested in them- which begs the question of what he actually did for Citigroup) back when he could do something about it. He was booted in January 2009.
I wanted to mention one other specific thing I remember about this attitude of bemused nonchalance in the face of the world crumbling. When Lehman fell, and the overnight lending market froze for some weeks leading to government intervention, there was a term for this at D.E. Shaw, attributed (perhaps wrongly) to Larry Summers. Namely, the term was “magic liquidity dust”, implying that all we needed, to solve the problems around us and the apparent irrational panic of the markets, was for a fairy to come down to us and shake her wand, spreading this liquidity dust generously in our otherwise functional and robust system.
The saddest part of all of this is that, in a very real sense, these guys were essentially right not to worry. There has been no real restructuring of the system that led to this, just its continuation and backing.
In my next installation I’ll talk about why I think people in finance were, and to some extent still are, so insulated from reality.
Meetups
I wanted to tell you guys about Meetup.com, which is a company that helps people form communities to share knowledge and techniques as well as to have pizza and beer. It’s kind of like taking the best from academics (good talks, interested and nerdy audience) and adding immediacy and relevance; I’ve been using stuff I learned at Meetups in my daily job.
I’m involved in three Meetup groups. The first is called NYC Machine Learning, and they hold talks every month or so which are technical and really excellent and help me learn this new field, and in particular the vocabulary of machine learners. For example at this recent meeting, on the cross-entropy method.
The second Meetup group I go to is called New York Open Statistical Programming Meetup, and there the focus is more on recent developments in open source programming languages. It’s where I first heard of Elastic R for example, and it’s super cool; I’m looking forward to this week’s talk entitled “Statistics and Data Analysis in Python with pandas and statsmode“. So as you can see the talks really combine technical knowledge with open source techniques. Very cool and very useful, and also a great place to meet other nerdy startuppy data scientists and engineers.
The third Meetup group I got to is called Predictive Analytics. Next month they’re having a talk to discuss Bayesians vs. frequentists, and I’m hoping for a smackdown with jello wrestling. Don’t know who I’ll root for, but it will be intense.
Debt
So I’ve been reading David Graeber’s book about debt. He really has quite a few interesting and, I would say, wonderful points in his book, among them:
1) Debt came before money, often in the form of gift giving (you can read about this in his interview with Naked Capitalism)
2) In ancient cultures, and even in more recent cultures before the introduction of money, there were typically two separate spheres of accounting: the first was for daily goods like food and goats, which worked on the credit system, and the second for rearranging human relationships. Here there were things like dowries and symbolic exchanges of gold, meant to acknowledge the changing human relationship, but not as a “price” per se – because it was understood that you couldn’t put a price on a human.
3) Money as we know it is intricately tied in with slavery because it was when a person became a thing that could be sold for profit that we had a sense of price and when these two separate spheres were united. In particular the existence of money also implies the existence of a threat of violence. Moreover, it is this “decontextualizing” of people from their homes, their communities, and families who are forced into slavery that allows us to measure them with a dollar value, and in general it is only through pure decontextualizing that we can have a money system. It is this paradigm, where everyone and everything has no context, that economists rely on to describe the standard game theory of economics.
4) There are three social structures that people come into contact with in their daily lives and in which they give each other things: communistic, reciprocal, and hierarchical. For example, among parents and children, it is communistic; among a CEO and his workers it is often hierarchical, and among two strangers at a market it is reciprocal.
Even though I could (and might) write a post on any of the above points, because I find them each rich with stimulating and challenging concepts (and I haven’t even finished the book yet!), I want to first describe something Graeber mentions about the last one. Also, if someone is reading this that thinks I’ve misrepresented Graeber’s points then please comment.
Namely, Graeber mentions that, although we each have experience, and maybe lots of experience, in the three different social structures, when we tell the story of economics and exchange we invariably talk about reciprocal exchange. So, for example, I have three sons and I spend way more time hanging out with my sons, attending to their needs and making sure their infected toes are treated and helping them find their raincoats, then I spend at any market. In other words, if I were tallying up my contributions to things, my kids would be a far greater drain on my resources than groceries. But our “story” of how we give things and take things is inevitably about buying stocks or negotiating for a house price. In other words, we have been trained (by economists?) or we have trained ourselves to define exchange as a reciprocal, Austrian schoolish, “be selfish and take advantage whenever possible” endeavor, even when in the face of it we can’t claim to be like that.
Since I’m unwaveringly interested in how one tells the story of oneself, this fascinates me. It’s a really excellent example of how we are blind to the most obvious things. It also makes me think that economic theory has a loooong way to go before it can really explain meaningful things about “how things work”. After all, when you allow yourself to include “personal feelings” in your definition of giving and receiving, you realize that the reciprocal exchange part of your life is actually pretty insignificant, and in fact if that’s all that economists can even hope to explain (and it’s not clear they can), then there is more left unexplained than explained.
Moreover, the fact that we don’t see this as a failing (or at least a major hole) in the economic theory, because we are blind to it in ourselves, also immediately points to the possibility that we have overemphasized this aspect, the reciprocal exchange sphere, in our current economic system. In other words, if we had a healthy understanding of how the other two systems work (communistic and hierarchical) we may have developed them in parallel with the reciprocal system, and we may well be better off for it. We may even have an economics system that doesn’t reward rich people and punish poor people, who knows.
By the way, ridiculous and ignorant critique of Graeber’s book here (as in he didn’t read the book) with rebuff in comments by Graeber himself. Thanks to a commenter for that link!
Some cool links
First, right on the heels of my complaining about publicly available data being unusable, let me share this link, which is a FREAKING cool website which allows people to download 2010 census data in a convenient and usable form, and also allows you to compare those numbers to the 2000 census. It allows you to download it directly, or by using a url, or via SQL, or via Github. It was created by a group called Investigative Reporters and Editors (IRE) for other journalists to use. That is super awesome and should be a model for other people providing publicly available data (SEC, take notes!).
Next, I want you guys to know about stats.org, which is a fantastic organization which “looks at major issues and news stories from a quantitative and scientific perspective.” I always find something thought-provoking and exciting when I go to their website. See for example their new article on nature vs. nurture for girls in math. Actually I got my hands on the original paper about this and I plan to read it and post my take soon (thanks, Matt!). Also my friend Rebecca Goldin is their Director of Research (and is featured in the above article) and she rocks.
Along the same lines, check out straightstatistics.org which is based in the UK and whose stated goal is this: “we are a campaign established by journalists and statisticians to improve the understanding and use of statistics by government, politicians, companies, advertisers and the mass media. By exposing bad practice and rewarding good, we aim to restore public confidence in statistics. which checks the statistics behind news and politics.” Very cool.
Guest post: The gold standard
FogOfWar kindly wrote a guest post for me while I was on vacation:
First off, for anyone who hasn’t seen the first or second round of “Keynes vs. Hayek” in hip-hop style, please check them out, they’re hilarious.
There’s an economic crisis going on around us, and periodically one hears people suggesting that we go back to the gold standard. It’s a pretty complicated issue, and I don’t really have an answer to the “gold standard debate”–just probing questions and a lingering feeling that the chattering class has been dismissive when they should be seriously inquisitive. I think this dismissiveness is driven by the fact that Ron Paul is the leading political proponent of the gold standard and competing currencies, and he’s (1) a traditional conservative libertarian (a bit in the Goldwater vein); and (2) a bit of a wingnut.
Aristotle would be ashamed— the validity of an argument does not depend upon the person making the argument, but upon whether the ideas contained are valid or invalid. Andrew Sullivan recently linked to this article by Barry Eichengreen, claiming that it’s “a lucid explanation of why calls to go back to the gold standard are so misguided.” In fact, it’s a fairly serious examination of the gold standard (ultimately coming down “nay”), which is a welcome relief from the flippant and arrogant dismissiveness one usually sees from economic pundits.
As with many edited articles, I recommend skipping the first page and a half (begin from the paragraph starting “For this libertarian infatuation with the gold standard…”). Here’s how I think the article should have begun:
[T]he period leading up to the 2008 crisis displayed a number of specific characteristics associated with the Austrian theory of the business cycle. The engine of instability, according to members of the Austrian School, is the procyclical behavior of the banking system. In boom times, exuberant bankers aggressively expand their balance sheets, more so when an accommodating central bank, unrestrained by the disciplines of the gold standard, funds their investments at low cost. Their excessive credit creation encourages reckless consumption and investment, fueling inflation and asset-price bubbles. It distorts the makeup of spending toward interest-rate-sensitive items like housing.
But the longer the asset-price inflation in question is allowed to run, the more likely it becomes that the stock of sound investment projects is depleted and that significant amounts of finance come to be allocated in unsound ways. At some point, inevitably, those unsound investments are revealed as such. Euphoria then gives way to panic. Leveraging gives way to deleveraging. The entire financial edifice comes crashing down.
This schema bears more than a passing to the events of the last two decades.
First, I would reword that last sentence as follows: This schema bear a striking resemblance to the events of the last two decades. Moreover, I would add, in light of this data, one might ask not why fringe candidate Ron Paul is calling for examination of a return to the gold standard, but rather why this view is considered to be on the fringe rather than at the center of debate. There are a number of reasons to believe that a return to the gold standard might not have the desired effect, although that certainly begs the question of what can be done to prevent future crisis on the order of 2008.
I’d place myself in the camp of “not convinced that the gold standard is the answer, but think it would be really hard to fuck up the economy as bad as the Fed did over the last 20 years even if you were trying, so maybe it’s an idea that deserves some real thought.”
Here’s another key paragraph:
Society, in its wisdom, has concluded that inflicting intense pain upon innocent bystanders through a long period of high unemployment [by allowing bubbles to work themselves out as Austrians advocate] is not the best way of discouraging irrational exuberance in financial markets. Nor is precipitating a depression the most expeditious way of cleansing bank and corporate balance sheets. Better is to stabilize the level of economic activity and encourage the strong expansion of the economy. This enables banks and firms to grow out from under their bad debts. In this way, the mistaken investments of the past eventually become inconsequential. While there may indeed be a problem of moral hazard, it is best left for the future, when it can be addressed by imposing more rigorous regulatory restraints on the banking and financial systems.
This gets to the crux of Eichengreen’s argument, but consider the following points:
- The “help” proposed by Keynsians in fact might make things worse in the long term (not out of malice, but the road to hell is paved with good intentions) by dragging out the inevitable consequences of misallocation during the bubble. In essence, this is a ‘rip the band-aid’ off argument. I think I’ve seen some historical analysis that the total damage done from a bank-solvency driven recession is, in fact, worse over time if extended rather than allowing banks to fail and recapitalize (Sweden vs. Japan).
- “… nor is precipitating a depression…” It’s taken as an article of faith that we would have been in a depression if not for the stimulus package, but I’m skeptical. This is and will always be a theoretical “what if” analysis, conducted by economists who have a cognitive bias in favor of a certain answer (and, for those working in government, a President who needs to juke the stats to get reelected).
- “While there may indeed be a problem of moral hazard it is best left for the future, when it can be addressed by imposing more rigorous regulatory restraints on the banking and financial system.” Whaaaaaaaaat? This is where Keynesians lose me. The sentence is so hopelessly naïve that it undermines the entire argument. Take your nose out of your input-driven models for a minute and take a look around and ask yourself how good a track record bank regulators have at imposing “more rigorous regulatory restraints” during boom times; major new regulatory changes only have political will during a crisis (Securities Act of ’33, Exchange Act of ’34, Glass-Steagall in ’34). I’m not going to argue the relative benefits of economic models when the theory is premised on a factual event that’s very likely not going to happen.
Here’s a paragraph I liked:
Bank lending was strongly procyclical in the late nineteenth and early twentieth centuries, gold convertibility or not. There were repeated booms and busts, not infrequently culminating in financial crises. Indeed, such crises were especially prevalent in the United States, which was not only on the gold standard but didn’t yet have a central bank to organize bailouts.
The problem, then as now, was the intrinsic instability of fractional-reserve banking.
This is a really good point; I don’t have an answer and it ties in to a lot of deep questions about the structure of the banking system and “what is money”. I do like that it’s being discussed, and I’d love to hear views (educated and layman alike) on “so if the gold standard won’t work and the Fed fucked things up so bad, what do you suggest?”
Lastly, here’s the end of the piece:
For a solution to this instability, Hayek himself ultimately looked not to the gold standard but to the rise of private monies that might compete with the government’s own. Private issuers, he argued, would have an interest in keeping the purchasing power of their monies stable, for otherwise there would be no market for them. The central bank would then have no option but to do likewise, since private parties now had alternatives guaranteed to hold their value.
Abstract and idealistic, one might say. On the other hand, maybe the Tea Party should look for monetary salvation not to the gold standard but to private monies like Bitcoin.
Um, for the record that’s long been the position of Paul. See for example this. Moreover, I think the author isn’t aware that there may be significant legal obstacles to create a competing currency.
I don’t have an answer to the many questions raised here, but they’ve been on my mind a lot. Any thoughts?
FoW
What is “publicly available data”?
As many of you know, I am fascinated with the idea of an open source ratings model, set up to compete with the current big three ratings agencies S&P, Moody’s, and Fitch. Please check out my previous posts here and here about this idea.
For that reason, I’ve recently embarked on the following thought experiment: what would it take to start such a thing? As is the case with most things quantitative and real-world, the answer is data. Lots of it.
There’s good news and bad news. The good news is there are perfectly reasonable credit models that use only “publicly available data”, which is to say data that can theoretically gleaned from quarterly filings that companies are required to file. The bad news is, the SEC filings, although available on the web, are completely useless unless you have a team of accounting professionals working with you to understand them.
Indeed what actually happens if you work at a financial firm and want to implement a credit model based on “publicly available information” is the following: you pay a data company like Compustat good money for a clean data feed to work with. They charge a lot for this, and for good reason: the SEC doesn’t require companies to standardize their accounting terms, even within an industry, and even over time (so the same company can change the way it does its accounting from quarter to quarter). Here‘s a link for the white paper (called The Impact of Disparate Data Standardization on Company Analysis) which explains the standardization process that they go through to “clean the data”. It’s clearly a tricky thing requiring true accounting expertise.
To sum up the situation, in order to get “publicly available data” into usable form we need to give a middle-man company like Compustat thousands of dollars a year. Wait, WTF?!!? How is that publicly available?
And who is this benefitting? Obviously it benefits Compustat itself, in that there even is a business to be made from converting publicly available data into usable data. Next, it obviously benefits the companies to not have to conform to standards- easier for them to hide stuff they don’t like (this is discussed in the first section of Compustat’s whitepaper referred to above), and to have options each quarter on how the presentation best suits them. So… um… does it benefit anyone besides them? Certainly not any normal person who wants to understand the creditworthiness of a given company. Who is the SEC working for anyway?
I’ve got an idea. We should demand publicly available data to be usable. Standard format, standard terminology, and if there are unavoidable differences across industries (which I imagine there are, since some companies store goods and others just deal in information for example), then there should be fully open-source translation dictionaries written in some open-source language (python!) that one can use to standardize the overall data. And don’t tell me it can’t be done, since Compustat already does it.
SEC should demand the companies file in a standard way. If there really are more than a couple of standard terms, then demand the company report in each standard way. I’m sure the accountants of the company have this data, it’s just a question of requiring them to report it.
The reckoning
There’s been lots of talk lately about how people are not having sufficient clarity of thought to be really creative any more; the argument is that they’re constantly interrupting themselves by reading tweets or their email, or of course crappy blogs, and never think about the big picture like they used to.
First, doesn’t it seem like every generation thinks that the kids of today are lazy? Doesn’t it just make us old fuddy-duddies to say stuff like this? Just because it’s a cliche doesn’t mean it’s not true.
Instead of complaining about young people, how’s this: a new way of having ideas is emerging, which is less individualistic and is therefore less recognizable to people who like to worship at the feet of “great thinkers.” There are more ad hoc communities being formed to explore ideas (like the Linux movement) and fewer larger-than-life personalities, but innovation and creativity are definitely taking place.
Okay, now that I’ve given those lazy-asses their due, I can complain about the obvious kinds of brain rot going on, mostly versions of lack of discipline and patience. I’m going to focus on a nerdy kind: the capacity for old-school reckoning (note how I’m even inserting fuddy-duddiness into the name).
Here’s the thing. It’s just too easy to google something when you don’t know it off the top of your head. There’s even some amount of feeling virtuous for bothering to scan wikipedia for, say, the population of the world or the prevalence of religions by number of worshipers. However, my claim is that it is better to delay the googling for at least half an hour.
Yes, I’m that guy who closes people’s laptops on their fingers and says, “hey let’s figure it out! Let’s not google it!!” Perhaps this explains why people don’t come to my house very often (please come back, you guys!). So yes, it’s come down to this: I torture my kids.
When my family has dinner, we have a rule that nobody can ‘use electricity,’ which includes watching TV or computers. We are also (obviously) super nerdy so we end up having pretty cool conversations (at least I think so!) in which we reckon.
Our reckoning skills, and our kids’ reckoning skills, have been getting honed this summer with the introduction of the daily ‘bonus question,’ which was our attempt to keep our kids’ brains from completely rotting over the summer while keeping things fun.
At first we would give them puzzles but later on they started asking us questions too. If the questions end up interesting enough (judged essentially by whether we all got genuinely into the discussion) then the kids win the prize of getting to watch TV after dinner until bedtime (don’t tell them but they’d get to watch TV anyway; and yes, they actually watch Netflix).
Turns out it is really fun to reckon with kids. For example one question our nine-year-old asked us is how thick a cylinder would be if it had to reach from the earth to the sun and was the same mass and density as the earth. We ended up googling something for that, I think the distance to the sun, but then again you can’t be crazy rigid!
The whole point is to realize you know more than you think, and you can figure out more than you thought you could based on estimates and a few facts. That, and to see how your biases steer you wrong. For example, when we were trying to figure out the number of people in each religion, we WAY overestimated the number of Jewish people. Then again, we live in New York.
One question I asked them which I thought was pretty cool, because they had such different and interesting answers to it, was how they could build the lightest bridge from our apartment to their school. There was no correct answer and that made it even neater, and it didn’t keep it from being a classic reckoning conversation.
So here’s my challenge: wait half an hour before googling something, and see how much you can figure out about the answer before you find it.
Back!
I’m back from vacation, and the sweet smell of blog has been calling to me. Big time. I’m too tired from Long Island Expressway driving to make a real post now, but I have a few things to throw your way tonight:
First, I’m completely loving all of the wonderful comments I continue to receive from you, my wonderful readers. I’m particularly impressed with the accounting explanation on my recent post about the IASP and what “level 3” assets are. Here is a link to the awesome comments, which has really turned into a conversation between sometimes guest blogger FogOfWar and real-life accountant GMHurley who knows his shit. Very cool and educational.
Second, my friend and R programmer Daniel Krasner has finally buckled and started a blog of his very own, here. It’s a resource for data miners, R or python programmers, people working or wanting to work at start-ups, and thoughtful entrepreneurs. In his most recent post he considers how smart people have crappy ideas and how to focus on developing good ones.
Finally, over vacation I’ve been reading anarchist David Graeber‘s new book about debt, and readers, I think I’m in love. In a purely intellectual and/or spiritual way, of course, but man. That guy can really rile me up. I’ll write more about his book soon.
I don’t want to live forever
Every now and then I meet someone who tells me they want to live forever. Whaaa? First of all, even if I were somehow forced to live forever, I simply don’t want to be around other people who have been living way too long. Haven’t they noticed that as people get older they tend to get more rigid and set in their ways? If we had to live with a bunch of 1000 year olds, how would we ever move past the weird issues they have about how women shouldn’t work or gays in the military? It’s a crucial fact that our culture is replenished by youth. Don’t want to lose that!! Eww!
Second of all, and more to the point I want to make, there really are people interested in this idea, and it always seems to me they are typically people that really should be focusing on living more now. What is actually going to be their plan if they suddenly were told, “hey, you’ll live forever starting now”? And if they have some awesome plan, why not just go for it? What is keeping them from making those decisions?
I have always had a great deal of admiration for people who do make those interesting and brave moves in their lives. Just this week an old friend of mine, who is a successful artist, told me she’s going back to school (at Columbia, so good for me!) to become a full time student in Narrative Medicine. If you don’t know what that means, then I don’t blame you, because I didn’t either, but what matters is that she is totally into it and that fucking rocks that she’s doing that.
Another good friend of mine is getting her Ph.D. in the ethics of nursing, after careers in energy and publishing. On the one hand I think she’s addicted to school, but on the other hand, how cool is that? To see so many different parts of the world? And by the way, if you think I’m disregarding things like money and kids, let me say that she is a single mom with two kids, and is still making this work. It’s just that she never decides not to do something because it’s hard – she’s all about intellectual curiosity and trying new things. Love her.
What would you study if you were to go back to school right now? How would you reinvent yourself?
Personally, I’ve always made my big decisions by asking myself, how will I feel on my death bed if I did or didn’t do this? It’s closely related to the other question I dwell on constantly, who am I and what is the story of my life? And it goes along with my advice post, where I pretty much always tell people to go for it or to do what they’d do if they weren’t insecure – good advice for oneself as well. I’ve actually gotten to the point of looking forward to my death bed, so I can swap stories with the people around me about the crazy shit I’ve tried. I know the chances of that working out are about zero, but it’s a nice thing to think about.
Going back to the idea of living forever: if I didn’t have a death bed to look forward to, how could I ever motivate myself to get my ass off the couch and try something new? It’s precisely because we have a finite amount of time to try things that it’s really exciting to be alive.
Good for the IASB!
There’s an article here in the Financial Times which describes how the International Accounting Standards Board is complaining publicly about how certain financial institutions are lying through their teeth about how much their Greek debt is worth.
It’s a rare stand for them (in fact the article describes it as “unprecedented”), and it highlights just how much a difference in assumptions in your model can make for the end result:
Financial institutions have slashed billions of euros from the value of their Greek government bond holdings following the country’s second bail-out. The extent to which Greek sovereign debt losses were acknowledged has varied, with some banks and insurers writing down their holdings by a half and others by only a fifth.
It all comes down to whether the given institution decided to use a “mark to model” valuation for their Greek debt or a “mark to market” valuation. “Mark to model” valuations are used in accounting when the market is “sufficiently illiquid” that it’s difficult to gauge the market price of a security; however, it’s often used (as IASB is claiming here) as a ruse to be deceptive about true values when you just don’t want to admit the truth.
There’s an amusingly technical description of the mark to model valuation for Greek debt used by BNP Paribas here. I’m no accounting expert but my overall takeaway is that it’s a huge stretch to believe that something as large as a sovereign debt market is illiquid and needs mark to model valuation: true, not many people are trading Greek bonds right now, but that’s because they suck so much and nobody wants to sell them at their true price since then they’d have to mark down their holdings. It’s a cyclical and unacceptable argument.
In any case, it’s nice to see the IASB make a stand. And it’s an example where, although there are two possible assumptions one can make, there really is a better, more reasonable one that should be made.
That reminds me, here’s another example of different assumptions changing the end result by quite a lot. The “trillion dollar mistake” that S&P supposedly made was in fact caused by them making a different assumption than that which the White House was prepared to make:
As it turns out, the sharpshooters were wide of the target. S&P didn’t make an arithmetical error, as Summers would have us believe. Nor did the sovereign-debt analysts show “a stunning lack of knowledge,” as Treasury Secretary Tim Geithner claimed. Rather, they used a different assumption about the growth rate of discretionary spending, something the nonpartisan Congressional Budget Office does regularly in its long-term outlook.
CBO’s “alternative fiscal scenario,” which S&P used for its initial analysis, assumes discretionary spending increases at the same rate as nominal gross domestic product, or about 5 percent a year. CBO’s baseline scenario, which is subject to current law, assumes 2.5 percent annual growth in these outlays, which means less new debt over 10 years.
Is anyone surprised about this? Not me. It also goes under the category of “modeling error”, which is super important for people to know and to internalize: different but reasonable assumptions going into a mathematical model can have absolutely huge effects on the output. Put another way, we won’t be able to infer anything from a model unless we have some estimate of the modeling error, and in this case we see the modeling error involves at least one trillion dollars.
mathbabe 