“Shiro dreams of sushi” is a great documentary, about a perfectionist sushi chef in Tokyo who earned himself three Michelin stars. Really worth watching.
In that documentary, a food critic says that great chefs have the following five qualities
- They take their work very seriously and consistently perform at the highest level
- They aspire to improve their skills
- They want things their way
He also says that what makes a great chef is to bring all of these attributes together.
This reminds me of Gladwell, who describes in his book Outliers that lots and lots of experience are often needed to be really successful. There is a certain air of perfectionism in the background, too. And interestingly, many great chefs or artists have learned their trait from scratch, even though they do now things that are really out of the box. The same holds for painters, for instance, like van Gogh or Picasso.
All this makes me wonder to what extent there is a connection to academia. Successful academics are very devoted, take their work very seriously and work very hard. They keep on learning, and they stay curious. They strive for perfection, they are ambitious. And it takes a long time and a lot of training until they are at that point (I still think that it really helps to be a good classical economist to do great work in behavioral economics. And that macroeconomists can benefit from micro theory and empirical skills.). Like it does for great chefs. So far so good.
As for impatience and stubbornness I’m less sure.
There is some impatience involved, but then, what makes an academic do good work seems to be to play the long game, to make sure that contributions are as good as they get. Attention to detail is important, and so is it not to rush. Now, if one thinks of impatience as being eager to move on, that may be true, and it may be related to academics often saying that they want time to do their research.
Last, stubbornness. Yes, academics sometimes want things their way, but what seems to be important is to strike a balance between that and what is useful to society and what the community values.
In the end of the day, there seems to be a connection to being a great chef I believe, even when it comes to those last two qualities. Academics do science for others, and the same holds for preparing a great meal. And if impatience means that one is looking forward to reaching perfection to finish a project before serving it to others, then that could fit too.
We’re sometimes accused of sitting in an ivory tower, feet up and writing in abstract terms about all kinds of things that are not directly relevant to the real world.
Well, there are academics like that, and there is certainly value to doing fundamental research that will be an important input more applied work done by others, but there are also many others.
For instance, they do consulting, and I have the impression that companies or institutions attach great value to this. There is nothing wrong with that, I believe, as long as it stays within limits and they keep doing research and keep teaching. To the contrary even, I see great potential that this will make them better teachers and researchers, because there teaching and research becomes more relevant to practice. Or they are involved in policy design and designing institutions within the scope of projects financed by third parties.
In a recent essay, Esther Duflo from the MIT has argued that attention to detail is not only interesting but really needed and useful. She suggests that economists should be more like plumbers. Worth reading, especially also for Ph.D. students who are making up their mind about the direction they want to go in.
I’ve earlier briefly described the benefits of using versioning software. In a nutshell, this is what professional coders use to collaborate and to keep track of changes they make to their code. Once you’ve set this up for conducting research projects, you usually don’t want to go back. See Gentzkow and Shapiro’s Practitioner’s guide for some guidance. Highly recommended!
I personally have used SVN for this, but over the last years Git has become more and more popular. I looked into it yesterday and it seems to me that it’s on the one hand more powerful than SVN and on the other hand easier to use. See for instance here for yourselves.
Einav and Levin write:
Hamermesh recently reviewed publications from 1963 to 2011 in top economics journals. Until the mid-1980s, the majority of papers were theoretical; the remainder relied mainly on “ready- made” data from government statistics or surveys. Since then, the share of empirical papers in top journals has climbed to more than 70%.
Isn’t that remarkable? I certainly was under the wrong impression when I was a Ph.D. student in Berkeley and Mannheim and thought that it’s all about theory and methods. Where does this come from? Maybe it was because one tends to see so much theory in the first year of a full-blown Ph.D. program, which is full of core courses in Micro, Macro and Econometrics, covering what is the foundation to doing good economic research. In any case, my advice to Ph.D. students would be to strongly consider working with real data, as soon as possible. There is certainly room for theoretical and methodological contributions, but this should not mean that one never touches data. At least in theory 😉 everybody should be able to do an empirical analysis. And for this, one has to practice early on. Even if one wants to do econometric theory in the end. But even then one should know what one is talking about. Or would you trust somebody who talks about cooking but never cooks himself? OK, I admit, this goes a bit too far.
After having said this let me speculate a bit. My personal feeling is that one of the next big things and maybe a good topic for a PhD could be to combine structual econometrics with some of the methods that are now used and developed in data science (see the Einav and Levin article along with Varian‘s nice piece). In Tilburg, for instance, we have a field course in big data, by the way, and another sequence in structural econometrics (empirical IO).
At the recent Netspar Pension Workshop I’ve been talking to Susann Rohwedder from the RAND Corporation. We talked about van Gogh and how he spent his youth in Brabant, not far away from Tilburg. The way he was painting at that time can be described as relatively dark and gloomy and not nearly as amazing as what he produced later in his life in the south of France, with the exception of the potato eaters, probably. Here, what dominates, arguably, is good craftsmanship. What I find remarkable is that he learned painting from scratch before moving on and developing something new.
Likewise, also Picasso first learned painting from scratch, producing paintings that were well done, but way more realistic that what he is known for now. Susann remarked that also for modern dancing people often say that one should first learn ballet dancing, in order to get a good grip on technical skills, before moving on. Interesting.
This discussion made me realize that there is a strong communality with my thinking about behavioral economics. There are many people who do research in behavioral economics without ever learning classical economics from scratch, and I always wondered why they do that. Standard economic theory is the simplest possible model we can think of, and it works just fine for many questions we may want to answer. There is of course lots to be gained by studying behavioral aspects of individual decision making, as recently demonstrated once more by Raj Chetty in his Ely lecture. But I think the best way to get there is to first fully understand classical economic theory and only then build on that. In passing, another thing that Chetty pointed out very nicely was that the best way to go about doing behavioral economics is probably not to point out where the classical theory is wrong—any model is wrong, because it abstracts from some aspects of economic behavior in order to focus on others—but to ask the question how we can use the insights from behavioral economics for policy making.
Yesterday, we had Mirko Draca over as a guest, also presenting in the economics seminar. Over dinner, he mentioned that there are two main lecture series that he would recommend when it comes to learning more about time series analysis and statistics in general. They are:
- Ben Lambert: A large series of undergrad and masters levels short videos, including time series: https://www.youtube.com/user/SpartacanUsuals/playlists
- Joseph Blitzstien: His probability course at Harvard which starts at the basics and then gos onto a lot of useful distributions and stochastic processes: https://www.youtube.com/playlist?list=PLwSkUXSbQkFmuYHLw0dsL3yDlAoOFrkDG
This reminded me of my wish to actually use online resources more actively myself. And I would like to encourage especially Ph.D. students to actively look for interesting content on the web. It seems to me that such web lectures are tentatively underused and underappreciated, and that we usually don’t take the time to watch them as if they were real seminar talks or real lectures. However, that may be a mistake, and by making use of these resources ourselves, we may actually learn how to use the web more effectively when it comes to designing courses.
This is more broadly related to the challenges faced by universities, as described in a piece published by The Economist earlier this year.
But it concerns also conference visits. For example, most people don’t know that the plenary talks of many conferences are freely available on the internet. See here for some nice examples. All of them are highly recommended.
Today in class, somebody asked a question in my panel data econometrics class. The question concerned the assumption of strict exogeneity and whether it was violated in the example I gave before. I replied that yes, it could indeed be violated, but most of the time, in one way or another, a model will be mis-specified and assumptions will not hold in the strict sense. What I meant was that in some vague sense, the assumptions was a good enough approximation (without me going into the details of my example, think of the correlation between the error term and the regressor as being almost zero).
That made me think again of Milton Friedman, who argues in a famous essay that a model should be judged by its ability to predict counterfactual outcomes, or in his own words, “to make correct predictions about the consequences of any change in circumstances”. Sometimes, this is what we are after, and this is referred to as a positive approach (being able to make the right predictions)—as opposed to a normative one (where we can talk about welfare and how one can maximize it).
That sounds reasonable at first. But can we really make such a clear distinction? Can’t we simply see welfare as the outcome we would like to predict? Of course, we always need a model to make statements about welfare, but then it could also be that all models agree on the direction of the welfare effects of certain policy changes and only differ with respect to the exact magnitude. Therefore, I prefer to think of a model as a set of assumptions that are for sure wrong in the zero-one sense. But the question is how wrong, and that depends on the purpose the model is meant to serve. So, it’s a matter of degree. If the model allows me to make fairly accurate welfare statements (and I can be sure of that for whatever reasons—this is the red herring here), then I can apply Friedman’s argument that it’s good in his sense, but then I can even use if for welfare comparisons, so it serves a normative purpose. In a way, all this brings me back to an earlier post and in particular the part about Marshak.
PS on September 19, 2014: There are two interesting related articles in the most recent issue of the Journal of Economic Literature, in discussions of the books by Chuck Manski and Kenneth Wolpin, respectively. In these discussions, John Geweke and John Rust touch upon the risk of making mistakes when formulating a theoretical model, and how we should think about that.