Interview – Daniel Mügge

Daniel Mügge is Professor of Political Arithmetic at the University of Amsterdam. His research analyses the political economy of macroeconomic indicators and the political origins of the formulas through which we calculate them (you can find details on the Fickle Formulas project website). In 2009, Daniel Mügge’s dissertation on European financial markets had been honoured with the ECPR Jean Blondel prize as best European political science dissertation of the year. He spent the first half of 2012 as a visiting scholar at the Center for European Studies at Harvard University; he returned to spend the whole academic year 2014/15 there, as well. Until March 2016 Daniel was lead-editor of the Review of International Political Economy.

Where do you see the most exciting research debates happening in your field?

What I find most intriguing right now is the question of how the technological changes that are sweeping through our societies will change our political economies. I must say, it is not something that I have studied before or that I study right now and I do not think that too many people are studying these things. But some people who, for example, study democracy are interested in the question of how Twitter changes political dynamics and opinion or majority formation. I wonder to what degree standard assumptions that we have about how political economies operate – tensions between capital and labour, the distribution of low-skilled, lowly remunerated, and high-skilled, better remunerated labour across the globe, the geography of these things – will be changed fundamentally through the digital and other technological revolutions. For me, this is a fundamental challenge to the way we think about our field and one that is super exciting and very much of the current moment.

How has the way you understand the world changed over time and what (or who) prompted the most significant shifts in your thinking?

In my PhD dissertation I wrote about the lobbying power of banks in European financial politics. I had a pretty strong sense that it was useful to think of investment banks as actors who had clear preferences and who are basically in a political boxing match against other actors who also had clear preferences. Then they would use political institutions and the like to get their way as best as they could. Even though I think there is a lot of that still going on, it has become much less the dominant way in which I look at and understand politics. Topics like the way we come to define our interests, the way that we develop an idea of what would happen if we were to change laws in this way or that way; our guesses who would benefit from more or less global trade and so on. I now see a lot more ambiguity and mist and uncertainty in the way that we think about these things and our own interests and political priorities than I used to before. I guess you could say that is more a constructivist way of thinking than whereas I was more materialist before. For me, this means that the formation of dominant ideas, modes of thought, and dominant conceptions of what is important in policy-making, which information is relevant or irrelevant, has become a much more dominant theme. Over time, I have moved away from this billiard-ball model of not only international politics, but also class or group politics.

The key event for me was this: I defended my dissertation in 2008, the year that Lehman Brothers disintegrated, when the financial crisis really gathered speed. In the years that followed, I devoted a lot of time to understanding the dynamics of the financial crisis and the regulatory response to it. Looking back at the things I wrote during the subsequent years, I can really see the shift happening: things that I wrote in 2009, 2010 still had a very mechanistic view – the Germans want this, the French want this other thing, and they go to Brussels and fight that out. The main questions for me and many other people at the time was: why did we see the reforms that we saw, why did we not see the reforms that we did not see, and why were financial reforms more limited than some people had expected or hoped? The dominant answer was that this was basically the financial sector pushing back against sweeping reforms that would reign in their power. I also supported this answer initially, but have become more skeptical of it over time. I still totally believe that banks are not big fans of restrictive regulation – even though there are moments when they could be. But it became very much clear to me that neither the banks nor financial regulators themselves knew very well which kinds of regulations would work for them. In particular when it comes to safeguarding financial stability or integrity of financial markets it is actually not obvious which rules regulators should adopt so we have a more stable financial system.

I then studied accounting standards in detail together with a colleague and PhD student of mine, Bart Stellinga. That research really showed that it was actually not clear at all who wanted what and what would be beneficial for which bank or public audience. There was a lot of uncertainty and belief-formation and this uncertainty was a much stronger force to explain the indecisiveness of the regulatory response than a concerted pushback by banks who knew exactly what they wanted.


You are a scholar of “numbers”. Why are numbers so important to understand national and international political and economic processes?

I think we have become completely used to the fact that economic governance, both nationally and internationally, means governance by numbers. If you look at an economics or finance ministry, but also at debates in parliaments or within an international organization, the touchstone of these debates, the basic building blocks of policies and of political arguments, are always economic numbers. Numbers about growth, employment, public debt, inflation, exports, imports, foreign direct investment and so on. So they have really become the lingua franca of how we talk, think about, and manage our own economies.

You might say that this is obvious and we are completely used to that. My task is to make this strange again, to re-ignite our befuddlement, our surprise that numbers are so powerful. We normally think about them just as objective representations of some economic reality. We talk about economic growth as if that was a clear property of an obvious entity that is called Germany or the Netherlands. Whereas, in fact, it is not only unclear what the “Dutch economy” actually is, where it begins and where it ends, but also how we calculate something like economic growth, inflation or public debt. There are many different ways in which these numbers could be put together. Depending on the formula you choose, it might benefit some people or countries and disadvantage others. So there is hidden political baggage to the way we calculate these figures. If you combine that with the strong force and the importance that they have for managing our economies, we see a world opening up where we need to investigate: why do we use the formulas and numbers that we use? And what are the consequences of using one set of formulas over another?

This interest in numbers is expressed in your five-year project Fickle Formulas that you lead at the University of Amsterdam. Can you explain the project?

The central question of the project could be summarized as: Why do we measure our economies the way we do? In that sense it is about the origins of these particular measurement forms. Why do we choose one formula rather than another? At the same time these things evolve over time as you start researching them and the project has also become about understanding this quantified mode of economic governance. As I mentioned earlier, it is something that we are taking for granted: of course, the finance and economic ministries work with numbers. The people they employ are largely economists, who know a lot about the economy, but are really number-crunching, trained economists in a very particular mode of thinking. We tend to see that just as a normal state of affairs and for a very long time, that was not the case. Back in the days, people who would staff the upper echelons of economic ministries would be diplomats or something similar with pretty broad training. To be an academically trained economist is a very specific and semi-recent phenomenon of the last decades. I think there has really been a scientization of economic governance, which is something interesting in its own right.

If there is no obvious immediate alternative to this way of managing complex societies and economies, I think it is still worthwhile to better understand this particular mode of running our own affairs and the blind spots that it introduces because we care about things that are hard to measure and consequently they drop out of official government policies. Biases are introduced because we do focus on some things that we measure and not on others. The things that are measured, get attention – the ones that are not, do not. In this sense it is also about re-politicizing the essence of this quantified mode of economic governance.

We often use macroeconomic indicators and statistics to measure relations between states, for example with capital flows or trade figures. How can indicators, but also the work that you are doing about them, help us to understand international politics beyond these numbers?

When we started out on this project, we had a strong presumption that economic numbers would often be manipulated to the advantage of the political masters. So governments would always massage unemployment figures – not completely fake the numbers, but twist the formulas in ways that would be beneficial to them. On the international scene this would mean that governments would always try to push, for example, figures about trade deficits or capital flows in a direction that would seem useful to them. That would give them political ammunition to say that trade was unfair or that investors from another country are invading them. In our research, we found very little evidence of that; much less than I had expected at some point.

To give one example, together with my colleague Lukas Linsi, we have analyzed trade statistics in detail and the clue there is that you get two sets of numbers for each trading relationship. For the relationship between the United States and Mexico, for example, the American and Mexican figures about imports and exports do not necessarily match. We find that there are often huge gaps between them. In this particular instance, the United States has for a long time been complaining about the trade deficit it has with Mexico. But according to the Mexican figures, the trade deficit that the United States has is even bigger than the American figures suggest. Normally we would expect that there would be a political bias in the direction of the country that feels like it is suffering the most would exaggerate these imbalances and the others would try to downplay it. But there we found an instance where it was the other way around.


This means two things. On the one hand, it is not just a simple instance of conscious manipulation, but we have to dig deeper into the precise origins of these disparities. On the other hand, it means that a lot of international political arguments, for example about trading relationships, are fuelled by numbers that are not nearly as hard as the protagonists seem to think they are. There is quite often a hidden source of ambiguity in international politics that for the most part goes completely unrecognized.

There is a lot of discussion on how globalization impacts the way we measure global economic activity and how accurate nationally based accounts and national statistics are. Can we still trust country-level data and use it for researching international politics?

There is a little bit of good news and a lot of bad news. We can trust these numbers more than we sometimes fear in the sense that many statistical agencies still try to do their best to collect numbers in line with international standards and try to give, as good as they can, a representation of economic activity. In that sense, I think there is room for trust, namely that this is a bona fide attempt to use international statistical standards.

At the same time, we find that the basic categories with which international economic statistics operate correspond less and less to the actual economic reality. These two basic categories are the idea that there are demarcated national economies that are internally pretty homogeneous; and the idea that we can think of international interactions as clearly identifiable cross-border flows of either money that is being wired from one bank account to another or containers full of, for example, car tires that are being shipped from here to there. There are certainly attempts to capture these things in statistics. For example, the component parts of a smartphone come from different countries, get assembled somewhere else, receive a little bit of added value there and then are sent to a third place where the packing takes place and so on. That becomes very hard to track. So there is the mismatch between the basic categories and building blocks of international economic statistics. This is, in a negative sense, a textbook-world where you have a map with countries and you draw arrows from one country to another – that is not how the global economy works anymore. So statistics that operate on that basis do not tell us much.

You could argue that this introduces quite a bit of error when we as researchers use large datasets about trade or FDI to fuel our analyses. What is even worse is when it introduces bias. For example, when profits are booked in secrecy jurisdictions or so-called tax havens just for the sake of lowering the tax bill: this systematically distorts our image of what goes on where. Or when we see that a lot of trade happen within multinational enterprises: in order to measure that, we rely on data that we get from these multinational enterprises themselves about their intra-company trade – so transfer-pricing becomes really important. Here, companies have a strong incentive to massage these figures in light of the tax bills that they will eventually attract. So whether that is still a useful representation of actual economic relations rather than something that is just useful from a tax perspective is really an open question.

Do you have an idea of how we can still use these or maybe even better figures, for example firm-level data?

I think that the example of firm-level data can be a very useful way forward. In a broader way, we have to be much more careful to think about whether the data that we have at our disposal with all the defects that it has, is still useful with respect to the specific question that we want to ask. So, for example, when we look at trade statistics: a lot of trade statistics that people use only concern merchandise trade, because services trade is notoriously hard to measure. But for some countries, services trade is really vital and it is asymmetrical: “rich” countries often import a lot of merchandise and they will be exporting services. So if you privilege and focus primarily on merchandise trade relationships, then you are systematically missing an important part of what goes on in terms of flows in the global economy.

Another example is whether I am interested in gross or net trade flows. For example, people have the idea that high economic interdependence – and that is often operationalized as trade volumes between countries – has a strong effect on the political relations between countries. Heavily interdependent countries will, in a nutshell, be friendlier towards each other. There we could ask whether that is also true for countries that basically act as conduits. The Netherlands, for example, has one of the largest harbors in the world in Rotterdam. Thus, a lot of the “trade” of the Netherlands is actually not trade by or with the Dutch citizens, but it is wares that come from China, arrive in the Rotterdam harbor, go immediately on the train or are shipped off to Germany. They never really touch the ground in the Netherlands, but the Netherlands is still shown as a highly interdependent country. Depending on what your exact hypothesis is that you want to test in a regression model, you could ask yourself: do I want this China to Germany trade through the Rotterdam harbor still in the dataset I use because that is part of the mechanism I want to study? Or would I rather want data that has somehow netted that out of the figures? I think that being more aware of the defects in the data, thinking more specifically about the potential distortions in the data, and whether that would have an effect on the analysis that I do, would already get us one step further.

Trump justifies large parts of his foreign policy approach, thus also his stance towards China, with bilateral trade figures and the trade deficit of the US. Does he have a point or is this based on a misconception?

There are two things worth noticing here from our perspective. China and the US report very different figures, even about their merchandise trade relationships. The American figures about their trade deficit with China are roughly 120 billion USD higher than the Chinese report on a year-to-year basis. That has partially to do with the fact that a lot of the goods that the United States import from China are either just channeled through China from elsewhere or contain significant components that come from somewhere else. For example, the screens in your iPhone shipped from China are actually being put together in Taiwan or Japan or somewhere else. So the whole value of the iPhone will show up on the American side as an import from China. Whereas, as far as the Chinese are concerned, all they did was just put all these different components together that came from somewhere else. So I think there is partially a misconception: a lot of the American trade deficit with China is actually a trade deficit with somebody else.

The other important thing here is that looking only at the trade relationship is a very partial conception of a bilateral economic relationship. To stick with the iPhone example: a lot of the profits of the iPhone, manufactured in China, still go to an American company, namely Apple. Apple might in principle have an obligation to see its profits on all these iPhones taxed by American authorities. If American authorities then facilitate global financial openness that allows Apple to park its profits somewhere else such that the American authorities cannot get their hands on what might be a fair share of the profits of Apple, which has profited from being able to produce this thing for low wages in China – then it seems that focusing only on the trade deficit is a very partial and skewed way of thinking about who benefits how much from this bilateral relationship. I think there is really something skewed going on there and in that sense it is unfair. As long as the Americans can finance their trade deficit, they should be really happy that they are apparently able to get so much more stuff from China, having the Chinese people produce all these things, putting them on a ship and sending them so Americans can consume them rather than the other way around. You could say that this is, in a way, a sign of strength and not of poverty that you can afford to do that all the time.

Can you outline the key findings of your research in relation to how policy-makers, scholars and the public should interpret and use macroeconomic statistics in the future?

The first very basic message is that they should take them with a grain of salt. I think that people generally underestimate how much ambiguity there is in these figures. What that means is that there is too much trust in economic numbers so people think this is the unambiguously best, hardest and most solid form of information you can possibly get. You will find that in policy-making circles, but also in academia, there is a hierarchy of quality of information; and numbers are generally seen as much harder than the impressions that you get when you walk through a deprived neighborhood. On the basis of our research I would say I am not quite sure the distinction is that stark. I think that quite often walking around different neighborhoods in a city can tell you more than some statistics that may also give a very skewed version of what is actually going on in that city. I think that is lesson number one.

Lesson number two is that policy-makers should also be more careful in the use of these numbers, because policy-makers, as well as citizens, expect too much from them. We tend to see them like arbiters in a football match between competing arguments – as if numbers would stand above different opinions and arguments people have and they could be the judge of who is right and who is wrong. We find that both in politics but also in science that this is aiming a little too high. Quite often, it does not work that way. Numbers are useful, often as rough indications, as long as we understand how exactly they have been put together, but being too invested and confident in these numbers can also feed the backlash against them. Then people say: oh yeah, it is just all made-up stuff. People then go for a post-fact, fact-free kind of position where they say: “if it is not rock-solid, then it all has to be made up and it does not really count for anything any more and anything goes. Numbers are then just another opinion”. This is, in my opinion, also not the case. I think statistics are often a solid attempt to represent the economic world around us, but they have serious limitations and specific strengths and we need to be aware of both. On the one hand that means taking them a little bit less seriously than we normally do, but at the same time it also helps to put them in the right place and therefore prevent some of the nihilistic backlashes against numbers that we see in different parts of society.

What is the most important advice that you could give to young scholars of international politics?

This might sound a little bit stale, but I think it is useful if people have solid faith in their own intuitions. I think you should in your analysis definitely be also a strong critic of yourself and you should not at all become complacent. However, if Mister Professor Big Shot tells you a story that really does not seem to make any sense, you might think: “I read this three times, but somehow I really do not get what is going on here” then I would indeed encourage people to have the faith in their own intellectual abilities to conclude that maybe there is not that much going on. If you feel that there is a whole herd of people stampeding off in a direction that looks like an intellectual dead end and you feel that this somehow does not seem to make sense, then have the courage to indeed seriously explore the possibility that it does not make much sense. Or, if you see something in the world around you that looks like a really exciting research topic even though nobody else seems to be paying much attention to it: I would not say stake your whole career on this one thing. But at the margin, for pushing the envelope in a crowded field, those moments when we have those intuitions and ideas that are worth exploring – frequently they are not going to lead anywhere, but I still think that is something that is really worthwhile. Sometimes trusting your own guts is a healthy attitude.

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