Political Risk

In August 2016, SAIS Europe will inaugurate a new Master of Arts degree program in political risk analysis – called the ‘Masters of Arts in Global Risk’. It is a thirteen-month program taught in Bologna, Italy that includes a three-month practicum as a final project. The goal of the program is to help students to develop the analytic skills in economics, political science, history, law, and international relations necessary to work with clients to understand the world around us. It is a practical application of the social sciences. And it is a great potential source of added value for our students. This degree is currently accepting applications.  The deadline for applications is 7 January 2016.

The idea of bringing political risk analysis into the classroom is something that I have been working on for some time. I have had the privilege of working with political risks consultants for almost twenty years now. For the past five of those years, I have been able to teach a course on ‘Risk in the International Political Economy’ with the very generous support of my friend Robert Singer (Bob), who is a long-time supporter of SAIS Europe.

Bob has made it possible for me not only to develop a new course but also to involve a number of friends and former students who have gone into different professional areas that use analytic skills like those found in the political risk consulting industry (and related areas in the public and private sector). We have a roundtable with leading figures from the political risk consulting industry this coming Thursday, 3 December 2015, and a keynote address later this spring. Both are in honour of my friend and former student Janika Albers as part of a very generous initiative of her SAIS classmates.

I will write more about those contributions in future posts. For now, I just want to highlight some of the underlying rational behind the degree. As a health warning, these are my own personal views and not necessarily those of my colleagues at SAIS Europe – including the academic director of the new MA program, Dr Filippo Taddei.

What follows is a short talk I gave at  the LUISS in Rome in December 2012.  Some of the illustrations in the talk date to that period and so will seem a little weird from a present perspective. Mario Monti’s fate as prime minister of Italy is pretty clear now, for example. Nevertheless, I think the basic argument about method and approach still works.


Political Risk: Setting the Stage

Erik Jones

Good morning. It is a pleasure to be here and an honour to share the panel with such distinguished speakers.

There are essentially three dimensions to political risk analysis. First, it is a business that brings value to clients. Although I will not dwell on this aspect, I do think it is worth underscoring because no-one who is in the business of selling advisory services can afford to forget it – and I hope one of the panellists with more experience on this practical side of the business will help us understand the keys to their success. I say this with all the humility of a professional academic. We do not like to think of ourselves as ‘selling’ our services. We give our publications and other intellectual property away for free. And we complain when our administrators place us under material constraints. Academia is not a business. But of course it is. We pay salaries, rent buildings, and consume resources. The money for all that has to come from somewhere and so we look to governments and students. Unfortunately, we are not very good at explaining the value we add that makes it worth the enormous cost. That value is there. It may not be transactional – and I tend to think of students as raw materials rather than as consumers – but the value added exists. We would have less to complain about if we focused more effort on explaining what we contribute; we ignore ‘selling’ our services at our own peril. But enough editorializing.

The other two dimensions of political risk are more interesting to an academic like me. One of these is time and the other is method. I will take both dimensions in turn.

Let’s start with time. What I find attractive about political risk analysis is that it is explicitly forward-looking. We do not have risks in the past. We only have risks in the present if by the present we actually mean the near future. So to study those risks, we have to think about the future even as we look at the past. The technical term for that is ‘prediction’. Political risk analysts are soothsayers.

Of course that sounds a little ridiculous. I always start the academic year by asking my students why they came to graduate school. We run through the usual list of credentialing, interests, career move, and so on until I ask them how many came to learn to predict the future. Almost no-one ever puts up their hands. But that raises an interesting question for a policy school like Johns Hopkins SAIS. Can you offer policy advice without predicting the future? The short answer is that you can’t. You have to couch your advice in an ‘if-then’ statement. ‘If you do what I propose, then I think this will happen.’ That is a prediction. Political risk analysts do the same thing, only they tend to displace the locus of human agency. ‘If politician X does Y, then I think this will happen.’ The structure of the argument is essentially the same.

I think if we were to think about it hard enough, we would find that there are ‘if-then’ type predictions littered across the mainstream of academic research. Think of the Taylor Rule in economics or the argument for central bank independence; think of the democratic peace. These are if-then statements that tell us how to target monetary policy instruments, how to improve the long-run inflation-unemployment trade-off, and how to identify the relative risk of war. I think if we thought about it hard enough, we would find that conversation is often dominated by ‘if-then’ types of statements. If I have desert, if I eat too much cholesterol, if I don’t exercise regularly, if I sleep more, if I hug my kids. I think you get the idea.

My argument at this point hinges on two ‘if-then’ statements that can be subject to research. They tell us what I think we would find both in the academic literature and in casual conversation. Both of them are predictions of a sort. They predict what we will find in the future if we look at the past. Implicitly they also predict what we should learn from the past to aid our understanding of the future. If I am right about the large number of ‘if-then’ statements in our communication about the real world, then prediction is more common than we like to admit; academic research is not all that different from political risk analysis is this sense – political risk analysts are just more open about their ambitions. I will take this position one step further. Academic research is only scientific if it makes predictions. All true scientists are soothsayers. And those who refuse to make predictions are not scientists.

This is where I make the pivot from time to method. Of course most of you will recognize that last statement as an appeal to Popperian hypothetico-deductive falsificationism. Essentially, Karl Popper argued is that what distinguishes science from other areas of human endeavour is that it can be wrong. Wrong in this sense is not the same as inaccurate. Inaccuracy exists in the present. If I say my shirt is black when you can see that it is white, that is not wrong in the sense of Popper’s argument. Of course it is wrong; but we will call this kind of wrong ‘inaccurate’ because it is not related to science. Popper’s notion of being wrong scientifically focuses on prediction. When you make an argument as a scientist you have to specify the conditions under which that argument could fail. Only then can you check to see whether it passes. Each pass does not make the argument ‘right’ in any permanent sense. We cannot foresee the future with perfection. Therefore, the appropriate reaction to a successful prediction is to specify more conditions to use in testing the argument. In this way, prediction begets more prediction. Along the way, we don’t arrive at the truth but we do build confidence in the argument.

That method sounds a lot like what we do on a daily basis in political risk analysis. We talk about what we think will happen based on the data available. But we also talk about what we will look for to know whether we have got things terribly wrong. This is where the if-then statements become important. I think Berlusconi’s party will support the Monti government in passing the budget – but if the PdL splinters in the next few days, then I cannot say for sure whether Monti will retain enough votes. I am not sure whether that argument makes any sense. As an American, I am always reluctant to discuss Italian politics – particularly in Rome. But I think we can all recognize the structure of the argument. That is the kind of argument we would expect to hear as part of political risk analysis. We expect to hear that kind of argument because political risk analysis is not opinion; it is science.

This notion of science is not going to be popular with this audience. Most of you do not see yourselves wearing white laboratory coats. You probably think there is a big difference between what we do and what ‘real’ scientists like physicists and astronomers do. You are right in the sense that these are different disciplines with different models for analysis and standards for demonstration. Moreover, those differences are important as I will explain in more detail in a moment. The problem is that such differences do not constitute a viable demarcation between what is science and what is not science. The distinctive characteristics of physics and astronomy give us important information about physicists and astronomers as a scholarly community; but they do not relieve political risk analysts of the burden of complying with the standards for scientific argument.

Let’s take the notion of ‘judgement’ for example. Cecilia’s excellent concept introduces the ‘judgement’ of the political risk analyst as a factor for consideration. I thought this was striking because most of the political risk analysts I know work outside their own comfort zones. They are often young and new to the profession. Their goal is to ramp up as quickly as possible so that they can talk to their seniors. Sometimes they even have to talk to clients. The people on the other side of the conversation may ask these analysts for their judgement of the situation, but that is not really what they want. What they want is a credible argument. The question is not just what do you expect in the future, but also why should I accept your view and how would I know if you are wrong. Usually these clients – whether in business, government, or finance – are seeking multiple assessments. Their goal is not to take the first story they hear but rather to compare one with another. You cannot compare judgements in any meaningful sense but you can compare arguments.

This is where I shift from Karl Popper to Imre Lakatos and from naive to methodological falsificationism. Lakatos’ point is that being wrong is not enough to kill an argument; we are going to stick with what we have until something better comes along. Here again think of the Taylor Rule and central bank independence; think of the democratic peace. We can all come up with examples where these things don’t work. But unless we come up with a superior alternative, we shouldn’t be surprised if we keep seeing these things on the menu. That is as much a part of science as it is a part of political risk analysis.

Political risk analysts have become adept at comparing scenarios. The goal is to map out alternative futures and then give some insight into how we might know which one will predominate as new information becomes available. This is what scientists do as well with competing hypotheses. The structure of the argument may be slightly different but the goal is the same and so is the temporal dimension. We don’t know which is better now even if we have good reasons to believe one view over the other; we may know better tomorrow under certain conditions. A good example from the physical world might by the Higg’s boson and dark matter. That took only forty years and billions of dollars to explore. Political risk analysts tend to work on shorter time horizons and smaller budgets.

The question to consider is what makes one argument better than another. This is where technique and discipline become important. The fact of the matter is that different fields of inquiry have different standards. Economists use numbers and formal modelling; political scientists use institutions and polling data; sociologists use surveys and focus groups; historians use archival materials. Of course these categories are not iron clad and everyone uses everything when it is available. But there are strong tendencies in different communities. For example, I tend to go to economists if I have a problem with time-series regression analysis; I go to political scientists or sociologists for work with panel data or factor analysis. The challenge is to make myself credible with each of these audiences. Then I have to go one step further and communicate across groups.

This cross-disciplinary communication is a challenge. Just like different groups have different analytic preferences, they also tend to have different assumptions and jargon. The trick is to communicate without saying something stupid, by which I mean inconsistent. That requires a lot of effort and thought. Nevertheless it is necessary because the problems we face are not structured to fit academic disciplines. This is true everywhere and not just in political risk analysis. The difference is that political risk analysts have clients. They cannot choose to focus only on problems that fit within a specific intellectual community; they have to accept to work on problems that their clients find important. The clients know that there is a miss-match here and they will often go to different firms with different problems. Just as in academia, some firms are more quantitative and others more qualitative; some are better at modelling and others at monitoring. But this selection process will never eliminate the requirement for combining techniques from different disciplines. Political risk analysts have no choice but to work hard to communicate across them.

Here is where we come back to the point about the differences between the hard sciences and the social sciences. We often think about political risk analysis as belonging almost exclusively in the domain of the social sciences. The problems that we encounter are often social in origin. That is does not mean, however, that we can only communicate across social science disciplines. Because many of the clients of political risk analysis are not social scientists. They come from engineering, management, finance, or business. Moreover, they need to be brought into the conversation. Ultimately, they also have to be convinced of the merits of the analysis.

This is often the greatest challenge – particularly when the goal is to generate repeat business or a longer-term relationship. That is why political risk analysts work so hard on writing, and why they invest so much in editing and presentation. Unlike my colleagues in academia, they do not hide behind impenetrable models or highly specialized jargon.

Ok – I am getting ahead of myself here. That is obviously not true, particularly when I start looking at the complicated statistical risk matrixes and compound indicators. But let’s be honest, it is much worse in academia, isn’t it? I mean, at least the people in political risk analysis try to be comprehensible.

I will wind it up here. The most surprising meeting I had recently was with the ‘methodologist’ at the U.S. State Department. It was surprising because I did not realize that the State Department had a ‘methodologist’. They do, and the reason is simple. State Department officials are political risk analysts. They have one client; it’s called the government. And they have to do make assessments of complex situations in the real world to meet the needs of this client. They have to make predictions. When they are good, they will also show when and how the client can know that the prediction is wrong. And they have to do all of this in plain language, through concise writing, under tight budget constraints. The job of the ‘methodologist’ is to teach analysts how to do that. That is how they bring value to the client. We academics are not much different. On a good day, we teach our students how to write and think clearly with limited resources of time and money. That is an important job not just for intellectual improvement, but also for good policy and profitable business. From that perspective, academics and political risk analysts have a lot in common. Thank you for your attention.