Progressives are likely facing a long time in the wilderness when it comes to the Supreme Court. Beyond the anti-worker, anti-consumer, anti-voter decisions already laid down over the past decade or so, we can anticipate a conservative majority for many years to come. But watching and waiting is no strategy at all. What can we do?
This one’s really for the economists. Just wanted to record some jumbled thoughts while I work my way through The Nobel Factor by Avner Offer and Gabriel Söderberg. It’s about the relationship between economic methodology and the sociology and political context of the discipline, through the lens of the economics Nobel.
Here is a nice review by Burton Malkiel of “Models Behaving Badly” by Emanuel Derman. The models of the title are from the world of finance: how are assets priced?
I am a layperson to the world of finance, so I find it difficult how to apportion “blame” for financial crises on faulty models or fraudulent inputs to them. Certainly history is littered with financial crises, so the influence of modern modeling alone cannot explain everything.
In any case, I just want to take this opportunity for a small lament that the beautiful act of modeling must be dragged through the mud by a financial crisis in this way. It would be fair to say that I am almost fanatical about the virtues of the concept and practice of modeling. I believe that modeling is inescapable. The world is complicated. Our senses deliver so much information, our mental apparatus must work so hard, that to process the world around us is to model. It is too much to ask that we understand everything; we have to understand a version of everything that is not so complex as the world.
This is also why economics works with models. We don’t have a scale replica of the world that we can play with to see how this affects that. We have to build a scale replica from scratch, using our best judgment to push insistently at the boundary between complexity (so that we can understand our model) and usefulness (so that we can make something from it).
In a way we are much luckier in economics than in finance. Progress in economic theory comes as our models are improved upon and refined, but we are more able to iterate forward because our models are not embedded in a Leviathan global finance industry that depends on their continued function. Creative destruction of old models is hard when the house comes down with them.
With all this in mind I want to highlight this passage from the review:
He sums up his key points about how to keep models from going bad by quoting excerpts from his “Financial Modeler’s Manifesto” (written with Paul Wilmott), a paper he published a couple of years ago. Among its admonitions: “I will always look over my shoulder and never forget that the model is not the world”; “I will not be overly impressed with mathematics”; “I will never sacrifice reality for elegance”; “I will not give the people who use my models false comfort about their accuracy”; “I understand that my work may have enormous effects on society and the economy, many beyond my apprehension.”
How many of these will I accept for economics? Certainly the first; the model is not reality. Certainly the second; math is helpful in model-building but is not the point of model-building. The fourth and fifth are hard to argue with.
The third I don’t like. Everything that we do must sacrifice reality. The test of a model is not its realism (a realistic model airplane would be no fun at all). All models are unrealistic because all models are wrong. Of course elegance is not the test of a model either, except that an elegant model is one that illuminates a relationship in a clear way by cutting to the heart of what matters.
Anyway, the point is that I think that “model” is not a dirty word. I feel possessive about “modeling” much the same way as I feel possessive about “rationality” – what they mean to me is important and wonderful and I hate to see them sullied by misrepresentation that stems from their overlap with the real-world ideas of modeling and rationality. I wish that all of the things like these could have their own words that are not borrowed from natural language.
Roger Goodell has joined the debate on the semantics of the word “economics”. Welcome, Roger! From ESPN (warning: obnoxious auto-load video alongside article) today:
Commissioner Roger Goodell wrote NFL players Thursday, outlining the league’s last proposal to the union and cautioning that “each passing day puts our game and our shared economics further at risk.”
Now what might “shared economics” mean? Of the several points of disagreement between NFL owners and players in the current conflict, one is, of course, money. I’m going to regretfully assume that the resource to which Goodell’s “economics” refers is only money. So what does he mean about that money?
He added, “Senator McCain – you can’t run away from your words and you can’t run away from your record. When it comes to this economy, you’ve stood firmly with George Bush and a failed economic theory, and what you’re offering the American people is more of the same.”
Only because it’s a really cool article and I just discovered it, and not because it really has anything to do with anything, “In The Air” by Malcolm Gladwell in the New Yorker is well worth a read. It’s about a fascinating organization called Intellectual Ventures, but there are just a bunch of fun little snippets. A couple:
This phenomenon of simultaneous discovery—what science historians call “multiples”—turns out to be extremely common. One of the first comprehensive lists of multiples was put together by William Ogburn and Dorothy Thomas, in 1922, and they found a hundred and forty-eight major scientific discoveries that fit the multiple pattern.
In the nineteen-sixties, the sociologist Robert K. Merton wrote a famous essay on scientific discovery in which he raised the question of what the existence of multiples tells us about genius. No one is a partner to more multiples, he pointed out, than a genius, and he came to the conclusion that our romantic notion of the genius must be wrong. A scientific genius is not a person who does what no one else can do; he or she is someone who does what it takes many others to do. The genius is not a unique source of insight; he is merely an efficient source of insight.
Surely it must be getting harder to have simultaneous discovery, in a world where research communities are global and transmission is very fast? Gladwell’s article goes on to talk about something like the standing on the shoulders of giants concept of building on the existing body of knowledge, speculating that multiples are proof of inevitability of inventions or discoveries.
What about economics? Are economic researchers building on existing research? It can sometimes seem that the questions we ask are so specific and arcane that the chance of simultaneous “discovery” is low; so much so that’s it’s tempting to wonder if we should even be using the word “discovery”. I wonder what, if anything, we could attach the word to in the history of economics? Obviously hindsight is 20/20, but it’s difficult to see what really was new or difference-making in the field, going back as long as you care. What has the discipline of economics done for us?
And what will it do, then? Gladwell’s article talks about a group of smart people who get together to try to invent stuff. What “economic” (however they choose to interpret the word) questions would such a group want to tackle, if any? Where is the innovation coming from in our field? I look at the list of Nobel prize topics, and, don’t get me wrong, I like finding things out for their own sake, but what are these things really doing, either practically, for the world, or simply for the body of knowledge we call economics? Of course we’re not supposed to be able to see where the next huge idea is coming from – that’s the whole point – but if we haven’t had a really important idea from, or even an important question for, academic economists for so long, if ever, isn’t a little pessimism forgivable?
I’m often tempted to argue, not entirely facetiously, that the methodology of economics and its status as a grounds for “discovery” was pretty much done 70, 80 years ago, and the rest is just application, gravy and statistical analysis. Even if that were true, would it be such a bad thing?
Down to dirty work today, as I make the bold claim to start talking about the guts of the economics profession. What are we up to? The first distinction in economics research methodology is ‘theory’ versus ’empirics’. Specialization has gotten to us in a big way here, in that theorists and empiricists don’t really associate at all.
So what’s what? Both methods are trying to attack similar questions – what happens if this changes, how do I achieve this, what is the relationship between these things – but use very different standards of proof. A theoretical ‘proof’ is to create a simplified model of reality to speculate on how the things might be related, while empiricists dig into big datasets to try to find the real-world relationship, the common problem being that things are pretty complicated. When economists talk about “applied economics”, they are using a label for the practice of statistical analysis of data in empirical economics research, so in some sense “applied” is not really an informative word here.
When we actually want to answer questions, say for policy analysis or just because we care, it is obviously smart to draw on diversity and explore the theoretical reasoning behind the relationship you’re interested in as well as whatever suggestive real-world evidence exists. Being that this isn’t what economic research papers do, this isn’t what economists do, though: we all do either one or the other whenever we write a research paper. Every economist is, first and foremost, a theorist or an empiricist (or both, but you see what I mean – they are distinct concepts at all moments).
The problem for empiricists is, in a way, harder than for theorists, because finding meaningful relationships in real data is surprisingly difficult, and assuming something away is a much more technical proposition when you have to kill it in your actual data rather than just in your abstraction. For example, if I see that the airport built a new terminal and that house prices went down, I can certainly argue that one caused the other, but actually proving it is a very different proposition. Econometrics is the branch of economics that tries to develop methods to analyze data where it’s difficult to infer causality. Of course, this problem is common to all statistical analysis, not just economics, and it is surely true that really strong evidence is revealed without fancy techniques.
A lot of economists do “applied economics”. Now this is going to be mostly just an anecdotal claim, but it’s certainly plausible to argue that the things that made economists decide to become economists seldom include a burning desire to trawl through huge datasets and run a bunch of regressions; the questions that can be answered in this way are interesting, sure, but the work itself is not a lot of fun. On top of that, despite the positivist teaching of economics, the proportion of time spent on the empirical methods is very, very small compared to the proportion of economics research that is empirical. Not that this is a bad thing: there isn’t a huge amount you can say about empirical methods before you’re actually in a position to use them (and again: not that much fun), but it might be presenting a drastically skewed picture of what it means to be an economist.
There’s actually a bit of a rift within empirical economics about the role of theory, which is a different matter entirely – I’ll try to paraphrase to the best of my ability. That rift concerns the seed of the empirical test being done – should it be explicitly associated with a theoretical model of the relationship you’re looking for in the data (that’s ‘structuralist’), or should the data be allowed to speak for itself and leave models out of it (‘reduced form’)? Now, the funny thing is that, as we know, it’s possible to write down a self-contained and consistent theoretical model that proves any relationship you want; the value of the model depends entirely on how you judge the value of its own little world. Thus, employing theory as some kind of dual proof while doing empirical work is actually redundant; it can offer some clarification of what you think might be driving the relationship you’ve found in the data, but it’s not especially helpful to say “hey, I found this empirical evidence – and look, the model says the same thing!”.
Which, again, is different from the idea of puzzling out a theoretical idea then trying to find evidence to see if it’s true or not. This kind of thing is actually not incredibly popular, perhaps because of the vastly different worlds theorists and empiricists orbit in – different methods, different seminars, different journals. The paradox is thus that very little empirical economics research actually tests theoretical economic hypotheses. Does each approach lend itself to different questions, never the two to meet, or is it in fact just that we don’t like following on each others’ coattails?
Back to the big point. Let’s say I’m a research economist and I’m thinking of a question like this: “would a national health service be good for the United States?” What I will not end up doing is writing an answer to that question, drawing on the arguments and evidence from a variety of sources. The economist’s role in answering such questions depends on which flavor of economist he is. The theorist might end up asking “how would it change the problem for an individual if they were faced with a national health service rather than the current system?” She might create a little model of a person facing choices between spending their money on health care or on other things, who goes on to interact with an insurance company in one instance or the new health service in the other, and figure out how that person’s choices might plausibly change.
The empiricist might end up asking something like “how does the size of a deductible affect people’s health care spending?”, since this might tell us something about the zero-deductible world of national health care, or “how do wait times affect health outcomes?”. Note that to answer the original question – should the US switch systems – using any kind of data, or indeed any kind of theoretical model, is staggeringly complicated and difficult.
Neither type of economist actually writes about the answer to the big question in their academic research. Instead, they go to the questions that their method might be able to answer, making just one brushstroke on the painting of the argument, and for theorists and empiricists, those questions are very seldom the same.
Time for round two of economics euphemism bingo! This time I was especially exasperated because the article in question (from BBC News) is about the China-Tibet issue, not about business or stocks or some place where the euphemisms hide something unimportant.
Let’s wade right on in:
“Some say that is not practical – that an independent Tibet would not be viable. It might struggle to cope economically.”
Wouldn’t have enough money? Lack of resources? Lack of infrastructure?
“A “one-country, two-systems model” is one possibility. So far, that model has gone well in Hong Kong – although Hong Kong and Tibet are at very different stages of development.”
OK, tell me about the differences, then. What’s a ‘stage of development’? Why is it important?
“When they revised their plans for Tibet in the aftermath of the late-1980s protests, China’s leaders thought a programme of rapid economic development in Tibet would stifle calls for political change.“
Economic development how? More money? More resources? More investment?
Tibetans are frustrated despite heavy economic investment.
Delete economic? What is economic investment? What is non-economic investment?
To me, it’s just lazy. Just say what you mean. The word “economics” is not a crutch or a bin into which you sweep all the stuff you don’t want to talk about. Just stop using it altogether.