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.
This goes to the ones who already know what they want to do, and it has to do with structural modeling. It’s about how to do this in Stata (of all places).
There are many reasons why you may want to use Stata for your empirical analysis, from beginning to end. Usually, you will use Stata anyways to put together your data set and also to do your descriptive analysis–it’s just so much easier than many other packages because many useful tools come with it. Plus, it’s a quasi industry standard among economists, so using it and providing code will be most effective.
So, if your structural model is not all that complicated, you can just as well estimate it in Stata.
Today, I want to point you to two useful guides for that. The first one is the guide by Glenn Harrison. This is actually how I first learned to program up a simulated maximum likelihood estimator. It’s focused around experiments and the situation you usually have there, namely choices between two alternatives. It’s a structural estimation problem because each alternative will generate utility, and the utility function depends on parameters that we seek to estimate.
Then, today I bumped into the lecture notes by Simon Quinn, which I found particularly insightful and useful if what you’re doing has components of a life cycle model. What I like particularly about his guide is that it explains how you would make some choices related to the specification of your model and functional forms.
Of course, there are also many reasons why you may not want to use Stata for your analysis. But in any case, it may not hurt to give it a thought.
Last week we saw another one of Apples wonderfully crafted presentations. What a choreography! But besides learning how to present really well (just observe how a lot of information is conveyed in a way that makes it all look so simple and clear), there was something special going on.
First of all, what was it all about? New iPhone models, Apple goes payment services (aka Apple Pay), and the new Apple Watch. Now, at least to me, it seems that the watch is dominating the press coverage. But let’s think about what may be going on in the background for the moment.
The iPhone needed an upgrade anyways. Bigger screens (the competitors had this already, and customers were asking for it), better camera, faster processor, and new technology that allows one to use the phone for super convenient payments. Good move.
Then Apple Pay. How smart is that? Apple positions itself in-between the merchants and the customer and every time somebody wants to make a payment Apple sends a request to the credit card company and the credit card company then sends the money directly to the merchant. Apple is not involved in the actual transaction, has less trouble, and cashes in anyways. Customers benefit, and merchants will want to offer the service. Good move, with lots of potential.
Finally, the Apple Watch. When you read the coverage, then you realize that the watch is actually not yet ready. Battery life is still an issue, and so is the interface. And maybe the design will still change. But there are four truly innovative features almost hiding in the background. First, it’s a fashion item, unlike all the other technical devices that are already on the market. Second, it has more technology packed into it, and third, it’ll have apps on it. Fourth, you can use it to pay, with Apple Pay.
So, what’s so special about this event? It’s all about network effects. I’ve worked on two-sided markets for a while, and there are three types of network effects that play a role. The first ones are direct network effects. These are the ones we know from Facebook: the more people are on Facebook, the more I like to be on Facebook. These ones play less of a role. The second ones are indirect network effects. They arise because app developers find it the more worthwhile to start developing apps the more users will potentially download them. This is why Apple presented the Apple Watch now. Starting from now, until the product will finally be sold, they can develop apps, which will in turn make the watch more attractive to consumers, so it will have positive effects on demand. But developers will already see that now and will therefore produce even more apps. Very smart, and all Apple has to do is to provide the platform, the app store, and cash in every single time somebody buys an app. Finally, Apple Pay. Similar model. The more people use Apple Pay the more merchants will use it, and this will make people buy more Apple devices so that they can use the services, and so on.
So, if you ask me, taken together this is a huge step for Apple. Not because the Apple Watch or the iPhone are particularly great, but because Apple’s business model is incredibly smart. Beautifully smart. And I haven’t even mentioned that sometime soon all the Apple mobile devices will be much better integrated with the operating system on their laptops and desktops. As they said in their own words, something “only Apple can do”.
Writing an empirical paper involves—next to the actual writing—reading in data, analyzing it, producing results, and finally presenting them using tables and figures.
When starting a Ph.D., one typically imagines producing tables by means of lots of copy-pasting. But actually, I strongly advise you not to do that and instead to use built-in commands or add ons that allow you to produce LaTeX (or LyX) tables. There are at least two good reasons for this. First, it’ll save you time, fairly soon, maybe already when you put together the first draft of your paper. But at least when you do the first revision of that draft. The reason is that you will produce similar tables over and over again, because you will change your specification, the selection of your sample, or something else. And you will do robustness checks. The second reason why one wants to automate the creation of tables is that it will help you make less mistakes, which can come about when you paste results in the wrong cells or when you accidentally put too many or too few stars denoting significance next to the coefficient estimates.
Here’s an example of one way to do it in Stata and LaTeX (I usually use Stata for organizing my data, matching data sets, producing summary statistics, figures, and so on). I think the way it’s done here is actually quite elegant. This post is also useful when you’re using LyX, by the way, because you can always put LaTeX code into a LyX document.
So far this is all about generating tables. But actually, the underlying idea is that you organize everything in a way so that you can press a button and your data set that you will use for the analysis is built from the raw data, then you press a button and the analysis is run and the tables and figures are presented, and finally you press a button and the paper is typeset anew. This is described very nicely in Gentzkow and Shapiro’s Practitioner’s Guilde that I have already referred to in an earlier post. On the one hand, this is best practice because it ensures replicability of results, but on the other hand it will also save you time when you revise your paper, and believe me, you will likely have to do that many times.
The following is a slightly altered version of a column I wrote for the December 2011 issue of our student newspaper Nekst.
In 1925, economist Henry Schulz wrote in the Journal of Political Economy that “The common method of fitting a straight line to data involves the arbitrary selection of one of the variables as the independent variable X and the assumption that an observed point fails to fall on the line because of an “error” or deviation in the dependent variable Y alone, the X variable being allowed no deviation.”
At first glance, one may wonder whether this can be right. Haven’t we all learned that we regress Y on X when we are interested in “the effect” of X on Y? In his article, Schulz was interested in estimating demand for sugar. He faced the problem that both, demand Y and price X were measured with error. In such a case, indeed, there is no reason to prefer one of the two regressions described by him to the other. Here, “errors” come about—people realized later—not only because variables are not correctly measured, but also because there were aspects of the relationship between prices and quantities sold in that market that were not explained by a simple model saying that there is a one-to-one relationship between prices and demand.
Here comes the role of theory, and it is fascinating to see in the literature how the following ideas developed. It all started with a book Henry Moore wrote in 1914, entitled Economic Cycles: Their Law and Cause. In there, we can find a regression of the quantity of pig iron sold on its price. The coefficient on price was positive, and Moore interpreted it as a “new type” of dynamic demand curve. Philip Wright, a Harvard economist, reviewed the book in the following year in the Quarterly Journal of Economics and explained that it is very plausible that demand for pig iron was very volatile, whereas the production technology, and hence the supply curve, was not changing much over time. Therefore, the shifts in the demand curve trace out the supply curve, and that is why we are estimating a supply curve when regressing quantities on prices.
A discussion followed and then, after more than 10 years, Appendix B of Philip Wright´s 1928 book The Tariff on Animal and Vegetable Oils contained two derivations of what we know today as the instrumental variables estimator. The idea is that when we regress quantities on prices and use factors shifting the supply curve as instruments for prices (e.g. weather conditions for corn production), then we will estimate a demand curve. Conversely, when we use factors shifting the demand curve as instruments (e.g. a change in value added taxes), then we will estimate a supply curve. Carl Christ provides more details on the history in his 1985 AER article.
One thing one can take away from this is that theory matters. Once we see the world through the lens of theory—here a simple model of supply and demand—we can progress in our understanding of it. It also restrains us, because not everything that can be done should be done. The above example shows that first, we need to understand what we are estimating when we regress one variable on another. This is guided by theory. If we do not know this a priori, i.e. before we have performed this regression, then there is probably no point in buying expensive data sets, collecting data, conducting experiments, studying asymptotic properties of the estimator, or developing more fancy estimation procedures. This is also what Marshak had in mind when he started his 1953 paper by saying that “Knowledge is useful if it helps to make the best decisions.” Highly recommended.
CHRIST, C. (1985): “Early progress in estimating quantitative economic relationships in America,” American Economic Review, 75(6), 39–52.
MARSCHAK, J. (1953): “Economic measurements for policy and prediction,” in Studies in Econometric Method, ed. by W. Hood, and T. C. Koopmans, pp. 1–26. Wiley, New York.
MOORE, H. (1914): Economic Cycles: Their Law and Cause. Macmillan, New York.
SCHULTZ, H. (1925): “The statistical law of demand as illustrated by the demand for sugar,” Journal of Political Economy, 33(6), 577–631.
WRIGHT, P. G. (1915): “Moore’s Economic Cycles,” Quarterly Journal of Economics, 29(3), 631–641.
WRIGHT, P. G. (1928): The Tariff on Animal and Vegetable Oil. MacMillan, New York.
It’s August, which means that students are finishing up their research master or master theses. Here is some advice that I give most of them at one point or another, and I think also Ph.D. students may not be aware of all of the following. I’ll focus on the form for now, and will talk about the contents of a good paper at a later point in time.
Let’s start with the very basics. You want to make your paper to be pleasant and easy to read in terms of the font size. My usual advice is to use a font like Times with a size of 11pt, to change the spacing to 1.5 or double space, and to use margins of 3cm in the top and in the bottom, and 2.5cm on the left and right.
Footnotes are usually placed after the end of a sentence, after the full stop. And acronyms should be defined before being used. You can do this by writing out the acronym and putting it in parentheses right after that. From then on you can use it. Particular sections or figures you refer to should start with capital letters. So, you would say “in the previous section”, but “in Section 3”.
Equations should only be numbered when you refer to them. Also, when you have an equation that is a “displayed formula” (so takes a whole line) and the sentence ends with that equation, then the equation should end with a full stop in it. When the text continues after the equation, then there should sometimes be a comma, for instance because the equation uses something that is defined afterwards using the expression “where”.
Overall, I think the best advice I can given is to be very careful so that the writing is of high quality. First of all, the English should be correct. There should not be any typos, and you should make extensive use of the spell checker. Then, the references should be in good order.
The following mostly applies to Ph.D. students in economics and related disciplines. When you’re writing papers, you should definitely use LyX or LaTeX together with BibTeX. Also references to figures, tables, equations, sections, and so on should be programmed so that when you change the structure of the document or insert a section or another figure, all the references are updated. This will save you a lot of time in the future, when you go through the 10th or so revision of a paper.
Generally, learn from others. In an earlier post I’ve already suggested that you should read papers in top 5 journals. Not all of them are well-written. But the chance that you get a well-written paper is higher than in other journals. Look at how introductions are structured, how the research is motivated. And spend a lot of time working out the arguments.
Andrew Chesher told me once, when I was visiting UCL as a Ph.D. student, that one may want to think about the following structure: this is what I’m doing > this is why I’m doing it and why it’s interesting > this is how I’m doing it > this is what I find. I think this is a great way to think about presenting research. He also said that academic papers should not have any superfluous written text and that for every word one should ask oneself whether it’s really necessary. Thereby, one can make text shorter and ultimately more clear.
Always make sure you use easy to understand and short sentences, mostly active tense, and that each paragraph roughly corresponds to one line of thought. But don’t be too mechanical.
Respect the reader by explaining well. Think of your reader as not being an expert on the topic you’re writing on, but as being smart and having a general education in economics. That way, you will not make the mistake of not explaining things that may be clear to you, but not to most readers.
And before I forget: many students write that “coefficients are significant”, but it should actually say that they are “significantly different from zero”.
If you want to learn more, have a look at my earlier post on the challenge of writing, where I also provide a reference to Silvia’s book. And if you’re interested in working some more on your writing, you may also want to consider having a look at a classic, the “Elements of Style“.
I’m from Southern Germany, and just as many of my friends who also came to live somewhere outside of Germany, one of the things I’m really missing on a Saturday morning is a fresh pretzl, with a bit of butter and maybe some cheese. I’m saying Saturday because I recall going to the bakery with my dad before breakfast to get some. And those bakeries were normally closed on Sunday.
We call them “Brezeln” in Germany, and since I’m from Stuttgart I will of course claim that they were invented in Bad Urach, which is not far away. Bavarians will claim that they are from Bavaria, and in the end nobody can say where they really come from. But just as wheat beer they have made their way and are now extremely popular throughout Germany. Which is not surprising because they’re the perfect snack as they are, or with butter, so you will certainly get them on the streets or at train stations, for instance. Sometimes, in the US or elsewhere one comes across something that is called pretzel and looks a bit like it, but it is actually not a pretzel, most of the time (they are not supposed to be heavy and they are not supposed to be too crunchy or dry–instead they are supposed to be crunchy where they are thin and soft where they are thick).
A pretzel is made out of yeast dough and the brown color comes from lye. So it can’t be that hard to make them I thought. It’s a bit like molecular cuisine because of the lye, though. I’ve been practicing making them myself, and I haven’t been too satisfied so far. But this morning I made some that turned out the way they should be, and in the end it’s actually not that hard now that I’ve found the right balance. So here’s the recipe. You’ll probably have to try yourself what works best, because ingredients differ across countries. So this one is optimised for Holland.
I’m going to make six of them. Here’s how we do the raw dough. I used a kneading machine, but you can easily do the dough by hand. On the picture you see that I put everything into a large bowl, in the following way. Start with 300 grams of standard wheat flour. Bread buns and the like usually have 2% salt relative to the flour in them, so add 6 grams of salt on the side. Don’t mix it with the rest of the dough yet, because it’ll slow down the yeast when we put it together with the flour and water. Put in half a pack of dry yeast (or 10 grams of fresh yeast), that’s something like 3 to 4 grams, a bit of brown sugar, a bit of butter (about a tablespoon), a bit of milk, and some tepid water, about 150 mills. On the display of the scale you see 467 grams, so that’s it.
You can see that I’ve put the salt to the side, as well as the butter and have dug a little hole in which I’ve put the yeast, the sugar and the water. Then I’ve stirred the water very gently so that the yeast dissolves. This will produce a watery pre-dough. It’ll form bubbles, so let it do that for 15 minutes or half an hour. Then knead the dough. It’ll be a fairly dry dough.
Form six pieces out of it. Then roll them so that they are about 10 to 20cm long. This is what you see on the picture. It’ll be hard to make them longer, so let the dough expand a bit more by letting it rest for 10 minutes. Roll and pull at the same time. Leave them a bit thicker in the middle (about twice a pencil’s diameter).
Now it’s time to form the pretzels. The picture shows how it works. There’s no need to rush, you can do this step by step. Place them on a baking sheet with baking paper underneath and wait. Now it’s important to wait a bit, so that he dough expands, but not too much because then it gets too fluffy and will be dry. I’d say wait about 10 minutes.
Now it’s time to put lye on with a brush. It took me a while to figure this out, but you can actually get lye from the Asia shop. In Chinese cooking, lye is used to make some noodles, for instance. I’ve thinned it down a bit with water and have also made sure that I go with the brush a bit underneath the raw pretzels. You can also use natron powder from the baking section, or natron lye from the pharmacy.
Finally, put coarse salt on top and use a sharp knife to make a cut in the thick part. Bake for 16 to 18 minutes at 210 degrees Celsius. Good luck!
The following post is a slightly altered version of a column I’ve written for our student newspaper (see page 8 of the November 2011 issue of Nekst). In Tilburg, we have a separate department of econometrics, which I am a member of.
Wikipedia says econometrics is the discipline that “aim[s] to give content to economic relations”, citing the New Palgrave dictionary in economics. A sub-discipline of it is theoretical econometrics, which is concerned with statistical properties of econometric procedures, among other things. When people say econometrics in the Netherlands, this is what they mean. But actually, econometrics is much broader.
The most prestigious journal of our field is Econometrica, and when you visit the website looking for the “aims and scope” of it, then you will find that it “promotes studies that aim at the unification of the theoretical-quantitative and the empirical-quantitative approach to economic problems and that are penetrated by constructive and rigorous thinking” (emphasis added; you find a similar statement for the Journal of Econometrics). Interestingly, the ultimate goal is given here, namely to study economic problems. Studying statistical properties of estimators is part of it, but ultimately we want to answer questions that are interesting in the eyes of economists.
This brings me to the general approach in economics. Most economists study economic problems by means of models. An economic model is an abstraction of the world, which is made to focus on just a few aspects of human behavior and interaction. It is phrased in terms of assumptions, and one can think of it as a tale. We tell a story, and thereby hopefully learn about the big picture. Econometrics is of course concerned with the empirical side to this.
When you read an applied article in the Quarterly Journal of Economics, then you will often see that people exploit, in one way or another, exogenous variation to learn something about the average causal effect this variation has on some outcome. They then call this a reduced form approach, because they don’t estimate what the mechanism is through which something has an effect on something else. But they then move on and interpret their findings, having particular mechanisms or a class of models in mind. For example, when interpreting findings on unemployment duration, they will interpret their findings against the background of job search theory. Traditionally, there is a heated debate between followers of this approach, and followers of the so-called structural approach to econometrics. The structural approach is to spell out an economic model, and to estimate parameters of that model. Having the estimated parameters in hand, one can simulate how people would react to a policy change such as a tax reform that has not taken place. In a recent issue of the Journal of Econometrics, Michael Keane propagates the latter, in a somewhat provocative article.
What may seem confusing in this context is that every structural model has so-called “reduced forms”, which one gets by solving the model and expressing some of the variables as functions of the other variables. Under the right assumptions, this yields an equation that is linear in parameters, and that can also be estimated. Then, one estimates structural parameters from a reduced form equation. But these are often not the same reduced forms people have in mind when they read the Quarterly Journal of Economics. So, one way to confuse them is to ask what the equation they are looking at is a reduced form of (but many of the authors will actually have a good answer to this question). In any case, Tilburg does not take sides in this debate. There are people who pursue the reduced-form approach, and people who pursue the structural approach, also within our department. The latter have recently formed a group, and you can check their website in case you are curious. In our seminar series, you can see the whole variety of things econometrics has to offer, and you are of course welcome to attend.
It feels like there are not many other countries in which you can effectively communicate with staff and students in English (except for countries in which English is the official language of course, but definitely not in Germany for example). And generally, living in Holland as a non-Dutch is a delight, which adds to that feeling. One can see in so many places that Holland is an open economy that has a long tradition of opening up to foreigners.
Today, there was an article in the Volkskrant, one of the major Dutch broadsheet newspapers, in which an unpublished study by the association of Dutch universities was cited. According to the article, one third of the post-docs at Dutch universities has a foreign passport and 15 to 20 percent of the tenured faculty (associate and full professors) come from abroad. One has to keep in mind that this is across all universities. My feeling is that the top US schools are much more international, but when one takes the average across all universities in the US, then the number must be much lower (I’d be grateful if someone could point me to some numbers).
Nevertheless, it’s worth having a closer look. One challenge that the article did not talk about, and did not present any data on, is that people come to Holland because Holland is able to offer a competitive net wage in the first 8 years (because of the so-called 30 percent rule, which states that 30 percent of the gross income is not taxed). But Dutch universities are not prepared to top up the gross salary after those 8 years so that the net wage stays the same, and therefore many people actually leave. I know of at least two full professors who deliberately started to look for a new job about 1 or 2 years before their preferential tax treatment expired. They talked about it openly over lunch. I believe institutions still have to learn here, provided that they want to retain international faculty beyond the 8 year grace period.
This is one of the reasons why it is actually not the case that international faculty also climb up in the organization and become sufficiently involved in the decision making as heads of departments, members of the university board, or in the university council.
The university council is the highest committee in which employees and students have a say. The university board has to seek its approval for all major decisions. But in fact, the university council is one of the few places on campus where all documents are still in Dutch and where the rule is not followed that as soon as a non-Dutch speaker joins a discussion one would automatically switch to English. This is why in the spring we have founded a new initiative, TiU International. We have won 2 out of 9 seats in the council straight away. I will join as one of the two members. First, in the next days we will have to find out how we can overcome the reluctance of everybody to speak English.
Another reason why Dutch universities are less international than they could be is that institutions are sometimes not compatible with what is the international standard. For example, it is deeply rooted in the societal norms, and hence in all kinds of regulations and customs, that the top wage one can earn at a university should not be too high (there is a recent decision on the payment of top administrators). This kind of regulation is also the reason why banks now move whole departments out of Holland.
Besides, it always strikes me as somewhat odd that in Holland, Ph.D. students are also counted as scientific staff. By international standards, at least in economics (as I have described in an earlier post), they are still students who first take two years of courses and afterwards work on their dissertation. They then go on the international job market, join another university and only after that publish their work as a member of that new university. So, the name of a Dutch university will never appear on their best publications. This is perfectly fine, because they still contribute to the reputation of the university they got their Ph.D. from. For instance, the international academic community knows that Ralph Koijen who was first at the University of Chicago and is now at London Business School got his Ph.D. from Tilburg University. Just in the same way as firms would know that somebody got his Master’s degree from our university. The major challenge for us is that we have to pay our Ph.D. students as if they were regular employees. And there is no reason why we should. Instead, we could also pay them a scholarship (which saves us the high social security contributions). At the same time, it is of course true that they do some work that resembles work done by a regular employee. For instance, in my department, a Ph.D. student is supposed to spend 200 hours per year on teaching-related activities. For this time, they should indeed be paid as regular employees. But not for writing their thesis or attending courses. So also here, the institutions in Holland are lacking behind what is the international standard, at least the one at top US universities.
Overall, I believe Dutch universities are relatively international on the surface, but there is a long way to go until they are really international. 80 to 90 percent of the students at Dutch universities are still Dutch, which means that they are not yet successful in attracting international students. Moreover, institutions in Holland and also within our university are not yet prepared to support us in systematically retaining top international researchers and let them participate in the decision making. There is a chance that involving international staff in the decision making will help to overcome the former failure. We have started working on it here in Tilburg.
The following is a slightly modified version of a column I’ve written for the July 2012 issue of our student newspaper Nekst.
Last time (see here, page 8), I’ve talked about doing a Ph.D. in economics, and that made me realize that there is one challenge students and professors share, namely the challenge of writing. As a student, one “fears the empty page”, one finds it hard to start writing, even though one already knows a lot about the topic, say when it comes to writing a thesis. Or a term paper. Instead, one does some more reading—it never hurts, and if one know what one is talking about, then writing is up is going to be really fast, right ;)—, and one says to oneself that whatever one writes one will have to correct in the future anyways.
Well, that’s all fine, but in the end it’s normally not the case that the last days before some deadline are like all the other ones. Rather, there is a rush of “productivity” to finish up what one has started—well, too late, in the end. I write “productivity” in quotes because if you are honest, then it resembles more finishing a project under a lot of stress, so one could have done better had one only started earlier. Another example of a deadline, by the way, is the date of the next meeting with your thesis advisor. Sounds familiar? It is surely familiar to many people who engage in writing, not only students.
So we’ve identified a problem. There is no easy way out, but actually, there is quite a bit one can do. A couple of years ago, a colleague pointed me to a book by Paul J. Silva called “How to write a lot: A practical guide to productive academic writing”. I liked it a lot and would like to strongly recommend it to you. The topic is not only writing, it’s also about organizing your work. And about getting things done. Actually, I’ve already mentioned the book in an earlier post.
The author starts by describing typical patterns, for example the “productivity rushes” I talked about before. Then he says that writing is hard work and motivates the reader to think a little bit about the whole process of writing and organizing one’s work. He does this in a very nice way, describing tactics and actions one can take.
Everybody has his or her own optimal strategy. He himself prefers to have a strict routine that involves getting up and basically going straight to his desk with a cup of coffee. Then he starts working immediately, without internet access, and by lunch time he’s done with the hardest part for the day. Then he does all the other things, like answering emails, participating in meetings, and talking to people. That way, by having 3 to 4 really productive hours early in the morning, he gets a lot of things done, just because he does this every single day. And after some time he could again take his weekends off, and this made him even more productive during the week. Another important advice he gives is that it’s important to prioritize and to do the things first that are most important, and not the ones that are most pleasant (like checking what’s going on on Facebook or in one’s inbox). My dad once told me that he always does the things first that he finds the least pleasant, another good strategy I think.
He also talks about the importance of making realistic plans (than one does not always have to revise) and that a first draft of something is never perfect. But all this is just a little glimpse into what I found an incredibly useful book. I think it’s a great investment to read it. You can easily do that in an afternoon, if you keep you computer shut down.
Silvia, Paul J.: How to write a lot: A practical guide to productive academic writing, American Psychological Association. (2007), Washington, DC, US.