Monthly Archives: January 2016

It pays to be first: Neil Armstrong vs. other astronauts on google ngram

My best shot at the google ngrams tool

Google ngrams comparisons between astronauts Neil Armstrong, Buzz Aldrin (both Apollo-11) and Eugene Cernan (Apollo-17)


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The Disease of Externally Funded Projects in Social Sciences

Academics are like hamsters, once they are within the mill, they don’t realize that there is a world out there, and that they run in circles. Most of my colleagues nolens volens are part of a rapidly spreading disease in which regular moneys for research and teaching are increasingly substituted with external monies from funding agencies, e.g. the EU framework programs, or national public or private science foundations. As a caveat I would like to make clear that not all projects are bad, and neither are all funding organizations. The question is also not whether to fund social science project at all, which dominates the US academic politics these days. This is about how social science is funded.

Increasingly, national governments and the EU try to stimulate ‘excellence’ by making universities competing over grants. Grant competition is supposed to act as a surrogate for market competition among private firms: let universities compete over money like companies compete over customers. Unfortunately, the analogy breaks down very swiftly. Here are a bunch of severe problems this logic entails.

Let me start with a simple observation: in social sciences – unlike perhaps in natural sciences – there are rarely economies of scale. Economies of scale mean that the bigger a firm or production line becomes, the cheaper and more profitable will be the product per unit. This explains why in certain industries with huge demand for machines and capital input, firms become very big (just think about Boeing or Airbus). Social science is not like that: you don’t need a lot of capital, and research projects can be small. All you really need is a job contract with some solid perspectives, a computer and some travel money that’s it. Unfortunately, this is not what most funding agencies are about. They love to give money to huge projects, with many partners and big sums (that’s true for EU framework programs, ERC grants, and many national science foundations as well).

Of course, there are exceptions to this. For instance, if you need to collect a huge amount of original data, you will need quite a bit of money. Funnily enough, funding agencies are not very good in identifying those projects. There is the story about Thomas Piketty’s project, in don’t know whether it is true, but I don’t think it’s very much off the mark. Allegedly, Piketty and his collaborators applied for EU funds to do archival research and setting up the empirical foundations for his famous book (see the database here). Apparently, the EU rejected the project proposal because it was not interdisciplinary enough. This would have been one of the few projects in which there are clear economies of scale: you need a lot of money to coordinate such an effort across countries. For such a project several millions of Euros make sense, for most others it doesn’t.

Why is sponsoring relatively large projects a problem? Because it works like a Ponzi scheme you recruit more and more junior researchers (mainly PhD students) to do the grunt work. However, these new cohorts of PhD students need a career perspective, and thus you will need even more project monies to convert the old generation into project leaders for even larger batches of new PhD students. This tendency greatly exacerbates the already precarious situation of junior- to mid-career academics at universities and research institutions: those with unstable income, high job insecurity, and the lion’s share of temporary contract holders among the high skilled in many countries. And as in any Ponzi scheme the scheme is only beneficial for those on the top of the Pyramid, but the last group of people entering it, will have to pay.

On top of this, handing over the decision of who gets what and why introduces new forms of biases and dependencies. Perhaps this is less of an issue in natural sciences, where it is harder (though not impossible) for politics to undermine neutrality. In social sciences, these biases are much more likely: Just as an example: the EU mainly funds research that is related to the EU itself. Why is this the case? If the EU’s main interest was really about creating the infrastructure for a knowledge-based society then social science research would tackle the most important challenges of its time, and not necessarily those confined to the EU itself (see one report here). Even more problematically, there is evidence that EU-funded projects also have a normative spin: they need to show that the EU is a good idea (here an English summary, here the German original.). Now, in many sense the EU is a pretty good idea, but it’s also pretty damning for any social science research project, if the general outcome of the project is already defined from the very beginning.

In think this is only the tip of the iceberg. Externally funded projects create lots of new forms of dependence and often the loss of academic freedom. Universities become the hosts of a bunch of successful grant-attractors which are de facto self-employed and need to lead huge projects and commandeering a lot of research assistants, PhD students, administrators and coordinators. There is also, I think, evidence for the Matthew effect: If you have attracted a lot of projects in the past, your chances are high you get many in the future. Again, since there are little economies of scale, I think the opposite should hold true: if you are already busy with some projects you shouldn’t get more. Some successful attractors will end up with many too PhD students to supervise, too many projects to oversee, with predictable consequences in terms of quality.

It is also quite funny to think that bureaucrats are capable of selecting and measuring the performance of these projects. Any decent proposal would never guarantee the outcome of a publication. You cannot guarantee that your article will end up in a top-notch journal or your book project with a prominent publishing house (unless you know how to rig the game, of course). Hence bureaucrats evaluate projects in quantifiable output measures: whether you turn in your reports on time, whether you deliver what you have promised: a battery of working papers, reports, financial statements etc. Hence, in terms of publication numbers count, but it’s good enough to promise 10 working papers.

I think it is high time that academics critically reflect on them being in a hamster wheel. They need to form a position on what type of research projects really make sense, when and why. Here are a couple of recommendations. Feel free to comment and add:

• Given that there are already too many PhDs in the market, design and fund projects also for PostDocs and those in the middle of the career.
• Make project lot size smaller, forget about adding too many PhD students or too much money for research assistance. Make them funds effectively financing positions not small research units. (PhD students should not work, but do their own projects anyways)
• Funding agencies should ask for a sustainable element of co-financing: make universities not only contribute to the project, but make them responsible for guaranteeing a career path. A realistic option of tenure etc. Otherwise, universities have a huge incentive replacing more and more permanent positions with temporary-contract holders.
• Make the projects more open in terms of topic, and give less guidance in what important topics of research are. The market of ideas usually takes care of that. You don’t need a bureaucrat out there to decide what science should do.
• Do the same thing for evaluation: the projects proposed should promise less, but therefore more output that is directly relevant to science and its dissemination.
• Reduce the number of projects and make them less important for scientific careers: externally-funded project are an interesting additional source of revenue, but if universities are run like private enterprises they are no longer doing the kind of independent, basic research you initially asked them to do. If people only care about attracting new projects, while on a current project, their dedication to the current project will be lukewarm. You shouldn’t expect too much of an outcome under such circumstances.
• What did I miss? I am pretty sure, lot’s of things.

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The Fleshy Reality We Live in: Why Social Sciences are Soft Sciences

Three years ago I was asked as a non-economist to give a talk about econ teaching in public policy. I gave my talk the title Making Econ a Softer Science. My econ colleagues were surprised because they thought econ needs to be more empirical and even harder, i.e. more scientific. Now, as a political economist, I like the new turn to empirical and behavioural economics. And I like (big) data, but as a public policy person I don’t think that the image of hard sciences is particularly helpful.

As a matter of fact throughout the years I have encountered two extreme and radically opposed ideologies in my field(s): Ideology A states that social reality cannot be measured and that statistics in social sciences is useless; ideology B states that social sciences will only be real sciences if they emulate X with X = econ < physics < maths. In other words: the harder the better. Needless to say that stated in such absolute terms I find both equally rubbish.

Ideology A is often found in the context of international relations and some spins of political theory. I don’t want to engage in an epistemological debate about how to approach social reality. I just think that, first, there is a lot of stats in social sciences and that, second, not all of it is bad (although I do admit that a surprisingly large share is really bad).

Ideology B is often found among my fellow regression-junkies in political science and economics. I think they suffer from the famous condition of physics envy. In a strong dose this condition is very dangerous. There is a lot of reason to believe that the global financial crisis was in part the result of famous macroeconomists too readily believing that they know the iron laws of monetary policy. In a field that is dear to me, microfinance, there are also examples of scientific studies (in top US journals) that have been used to legitimize the boom in the industry although these studies later on turned out to have severe methodological issues. The politics of evidence based policy making is all too clear in these examples.

I think, given the ‘nature’ of social reality, social sciences have to be soft sciences. I also think that there is nothing wrong about this, as long as you admit it. I hasten to add that there is nothing wrong with scientific rigor. Trying to be precise, transparent and to talk in a language anyone could understand is good practice in all fields of social sciences. However, the only perfect case of rigor in the social world is rigor mortis. You kill the subject if you scrutinize it too hard.

For me social reality in social sciences is (ontologically) soft or, more precisely a mixed bag: some bits are tougher and lend themselves more towards counting and estimation, others not so much. The best simile for me is to compare social reality to the human body. Yes, there are many parts that are hard and easily measurable, such as the spine or the rib cage. These parts are hard enough to make them quantifiable. They also give some structure and stability to social reality. But that doesn’t mean that there are universal: bones can rot, rips can break. Similarly, there are relatively few hard laws in empirical science (can you name one? I sometimes believe there is one between violence and inequality link), and even these don’t exist in all temporal and spatial contexts.


Looking from the outside, what defines the human body is its organic shape, its fleshy bits, which vary from person to person, and even from day to day. These bits are harder to measure precisely, but they are still an important part of social reality. Nobody would be able to identify a person by looking at her bone structure. Similarly nobody would be able to identify a country or a political party without the fleshy bits, there idiosyncratic leaders its contorted history.

And in similar ways to good (holistic) human medicine, social reality is best understood using the diversity of methodological tools at your disposal, including quantitative and qualitative methods. This also implies that instead of squeezing social reality into a methodology that was developed for the physical world, methods in social science have to adapt to its own reality. This might also imply that we have to say farewell to the idea of cumulative knowledge, since social reality is reflexive and mobile and includes sciences themselves. But it is better to embrace this softness and deal with it, than conflating a skeleton for a human being.

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I dare anyone to find a more self-deprecating preface than my own!

Here is the preface of my 2009 book:

Defining ‘taxes’ is notoriously difficult. This is both a sobering and a
cumbersome fact. Many people would say that tax experts are the most
boring people they could think of; next to scientists perhaps, which makes
scientists working on tax issues to be the least sociable people on earth!
But – and this what this book hopes to achieve – such difficult to define
concepts are fascinating and totally remarkable objects for social scientists.
Conceptual ambiguity infuses power, politics, and even violence. I have to
be violently short with my acknowledgements, since there is a great many
people to thank. I am indebted to Daniele Checchi, David Carey and
Reimut Zohlnhöfer for letting me use their data, Pablo Beramendi, Thilo
Bodenstein, Michael Bolle, Christian Fahrholz, Robert Franzese, Steff en
Ganghof, Jacob de Haan, Torben Iversen, Philip Manow, Thomas Meyer,
Michael Neugart, Thomas Plümper, Francis Rosenbluth, Fritz W. Scharpf
and Michael Wallerstein, for helpful comments at various stages of this
book project, and my two supervisors Günther Schmid and Bernhard
Kittel for their help. I am most thankful to Luicy Pedroza, who shared
here intellectual insights and unlimited patience with me. All remaining
errors are mine.

And here comes the commercial part: Edward Elgar

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Here is the syllabus for my class on “International Politics of Development” Comments are highly appreciated

This year I will start a new PhD class at CEU on the “International Politics of Development”. You can find the syllabus here (The international politics of development PhD course Kemmerling Preliminary). Any comments on topics, literature etc. are highly welcome. It’s the first time I teach this class on the PhD level, so things are still very much in motion.

Best wishes


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