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An Interview with Dr. Ferre de Graeve, Researcher at Sweden’s Central Bank

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Dr. Ferre de Graeve, a researcher at  Sveriges Riksban (Sweden’s central bank), visited CERGE-EI back in November to discuss fiscal policy in contemporary DSGE models. Check out the hitherto unpublished interview between Dr. de Graeve and CERGE-EI PhD student Liyou G. Borga

Why did you choose to study economics? What motivates you?

At the age of 18, going out of school, I didn’t really know what I wanted to do. I decided to study but I pretty much eliminated subject after subject, and what was left was economics. After about three or four months of studying economics, I knew I wanted to do research in the field. And here I am. No regrets so far.

How do you approach your research interests?

I tend to not take advantage of economies of scale. I switch topics a lot. And that’s essentially because I want to learn more. Certainly there are disadvantages to that. But it’s so easy to get excited by something entirely new. I just pursue ideas that I find interesting.

At any point in time I have a couple ideas that I don’t pursue but I think about. But if let’s say a year later they’re still in my head, chances are I start working on them.

What are you working on now?

Essentially I am presenting at CERGE-EI an old paper of mine in which we were concerned with the term structure of interest rates. Now I am revamping that paper from a different angle. There has been a lot of discussion lately about fiscal policies, for obvious reasons. What we realized is that, although the paper has nothing to do with fiscal policy, essentially it turns out to be quite informative about what people call ‘fiscal inflation’. This is the probability that inflation is going to be driven by fiscal policy in the near future. So we basically estimated the model, and it turns out that the model immediately speaks to this issue. It was only two weeks of research, and all the results are already there. That’s definitely not my usual experience.

How did that happen? Is it just that you got lucky? Why did you decide to go back and look?

In terms of work, we got lucky, because we didn’t have to do much. There are a couple of people these days who warn us about fiscal inflation, and perhaps it’s the sense in their arguments that made us go back and look.

One big area for our students is macro models. This DSGE model comes up often in macro issues. Can you tell us in laymen-terms what this model is and why it is different from traditional models? 

DSGE models are essentially models that describe business cycle fluctuations and are built from what we call “micro foundations”; deep structural parameters that determine economic agents’ behavior.

Let me distinguish DSGE models from reduced form forecasting models on the one hand, and old-style structural models on the other. DSGE models complement both. Regarding the first set of models, the important part is that the DSGE models allow us to understand something that the forecasting models don’t necessarily do. Both models will produce forecasts. But DSGE models build on mechanisms that have an economic interpretation. The big advantage that implies for example for central banks is that they allow storytelling. You can think about policies and how they will work. You can create forecasts conditional on policies and that’s a lot harder to do in traditional reduced-form models.

The old-style structural models had the same objective as DSGE models. However, the way they were constructed was rather ad hoc, often inconsistent with theoretical models. They therefore produced answers to policy questions that were hard to put faith in.

How risky is it for central banks to adopt this model and base their forecasts on it, if we base our micro-foundations on some assumptions that don’t work?

I think the main risk is over-estimating the value of a model. I mean as soon as you have a model that you can use to tell stories, you may have too much attention for that model and you may stop thinking about model uncertainty. Because there are other models out there we are not studying. The current crisis is hopefully teaching us that we should think about other models too. In building a model and using it for policy analysis, there are a lot of steps we take that involve assumptions that won’t do well in the current state. We often fail to generate crises in these models of the type we see in reality. That said, I think there is a lot of value in being able to communicate clearly through a model. I definitely think there is value in DSGE models and how they are constructed, but we shouldn’t think that we’ve solved everything here.

As students, we always want to know advice about how to pursue research.  Do you have any advice?

I ask myself that question everyday, and I don’t know. The most important factor, I think is this: pursue your interest. In research you never know where you’re going to end up. You’ll always find out that things don’t work out. So you better have inherent motivation to get you going, even when you face those stages of research.

If it’s your interest, you’re kind of blessed to have the opportunity. For me, it’s certainly better than a lot of alternatives. The fact that I can think of something I find interesting and follow up on it—the average job doesn’t necessarily give you that.

Following the literature might give you more of a probability of getting publication success, but maybe there is value in thinking completely differently and pursuing it.

What is economics now, in terms of importance and relevance, compared to the past?

Well the field is getting so big now. When I started, I had the impression that everyone was doing macro and everyone was discussing with everyone. Now it seems there are so many fields, and they have all developed so much, so it’s hard to know about everything.

So is that a good thing? 

Perhaps with more fields we are inclined to go deep in every particular field, but then you can easily lose track of the bigger picture. It’s important that at least some people keep a bird’s eye view of all the fields, and question whether we are focusing on the right questions. Of course sometimes reality does it for us, such as with this recent crisis.  Afterwards, a lot of researchers I know thought ‘am I working on the right topic, given that current events are so massively important?’. Of course having many different fields can help people learn from each other and build.

Economics is indeed very diversified now, but there are some important questions unanswered. What research questions should be best pursued today by students?

Although we have models that have evolved to understand crises, we’re not there yet. I’m sure there are a lot of questions that are unanswered relating to all aspects of the financial crisis: both what’s happening in the financial sector, how it related to what’s happening in the real economy, and feedback between the two. There are a lot of things we don’t know in this area.

Mostly our students are from transition and developing countries.  In terms of research, what is the comparative advantage of a student coming from one of these countries?

Well for one thing, they probably have more observations and experiences with crises than Western Europeans have. I see that roots are important in research. But having had a good education will basically allow you to do anything. So then it comes back to interest. I don’t necessarily see a comparative advantage, it’s just that there is some correlation about what you think is important and where you come from. But given a good education, you’re basically going to be able to do whatever you want, because you have a wide understanding of issues and the ability to learn.

Author: Liyousew G. Borga, 2nd Year PhD Student

9 November 2012

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Development Economics Website Launched by CERGE-EI Faculty and Students

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A new website popularizing research in development and behavioral economics recently launched, and CERGE-EI faculty and PhD students are intricately involved. You can check out the new site here: www.gapiresearch.org.

The Group for Analysis of Poverty and Inequality is behind this project.  The group was founded by Michal Bauer (CERGE-EI) and Julie Chytilová  (Charles University), along with their PhD students Vojta Bartos, Jana Cahlikova, Lubos Cingl, Dasa Katreniakova, Ian Levely and Tomas Zelinsky. Their main aim is to bring applicable economic research to a broader audience, while also bringing researchers interested in these issues closer together.

The group is interested in understanding the roots of human poverty and inequality within and across societies. They explore crucial issues of economic development, and their research is often inspired by insights from behavioral economics and psychology.

Does the experience of war cause people to cooperate more within their groups? What can this mean for post-conflict recovery? Why are poor people willing to pay for the opportunity to save, and how does microcredit help them in this respect? Are women more patient in their decisions than men, and what does it mean for targeting development aid within a family?

These difficult questions largely require tools from experimental economics. The researchers collect original experimental data in many countries ranging from the Czech Republic, Slovakia, Georgia, Uganda, Sierra Leone, all the way to India and Afghanistan.

If you are curious about the answers to these questions, you can dig deeper into this exciting field at www.gapiresearch.org or check regular updates at their facebook profile: http://facebook.com/GAPIresearch.

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Searching for Answers with Dr. Adam Sanjurjo

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Dr. Adam Sanjurjo (University of Alicante) researches search behavior in ‘rich search environments.’ As he writes in a recent paper, “we consider much more than wage when choosing a job, and much more than price when purchasing a home.” How to optimize the way we search and the ultimate decisions we make is still not well understood, but Dr. Sanjurjo is searching for answers. Check out Dr. Sanjurjo’s brief interview during his visit to CERGE-EI in Prague:

Who is someone who influenced your decision to become an economist?

Well maybe I shouldn’t say this, but I think it was more of a compromise. For a lot of people who do economics, it’s a compromise, because they like math and they also like psychology. And that was the case for me. As an undergrad I wasn’t as enthusiastic about my economics classes as my math classes. But economics allows the whole continuum between pure math and pure psychology. I felt that I could have that whole continuum to work with, and I think it was a pretty good strategy.

What are you working on now?

Many things, but my main line of research is search behavior in rich search environments. So for example you want to choose a house, so you’re going to evaluate a bunch of different houses on a bunch of different dimensions.  How do you search that information, what information do you search, and in what order do you search that information? And how does the order that you search in affect the choices you are going to make?

I’d say a lot of my work falls under the veil of heuristics and biases, relating to the original work of Kahneman and Tversky in their 1973 paper. It has a lot to do with how people think about probabilities, and how they have systematic biases in the way they evaluate subjective probability.

Where do you get inspiration to start doing your research?

Well that’s pretty clear. The reason I went to do PhD in economics is because I was kind of obsessed with the idea of modeling first impressions. Somehow that quickly turned into the study of information overload.  I felt like information overload was affecting me in my life and a lot of people around me. It was very salient for me. And actually the rich search stuff I’m doing now stays true to that. I think I’m approaching it in a slightly more responsible way than when I first started. So now I’m trying to find concrete mechanisms that cause people to get confused or overwhelmed in complicated decisions, and even in decisions that aren’t so complicated.  We get confused.

So this research can be applied quite generally?

With the multiple attribute search stuff, I think this is a framework that applies to all decision making. So we’re not aware of the fact that we’re evaluating choices on multiple dimensions, and yet we’re doing it all the time. We’re integrating multiple dimensions in evaluating what decision to make, for every decision we make, and we’re making decisions constantly. I’m making decisions right now; I’m choosing which words to use, which sentences to string together, which topics to talk about

And what are your main concerns?

Sometimes I get the feeling when I present this material that it’s very interesting, but it’s also very eclectic for many economists. I personally don’t think it’s eclectic at all, because I think it applies to all decision making. But it’s eclectic in the sense that not much formal work has been done on it, because they’re very rich search problems and thus hard to study. So people have steered away from them because they’re hard problems to work with. But that doesn’t mean they’re not important problems to study.

How do you think your field will look in ten years?

I don’t think it will look very different. There’s kind of a ‘holy grail’ in my field, which is to be able to analytically solve what’s the best way of searching in these rich search problems. I don’t think that’s going to happen any time soon. However I think the ability to numerically solve for what’s the best way to search in these environments is going to get better. People are going to do impressive things in terms of having these very rich search problems that are similar to the way that people search for information on the internet. So you’re not only going to be able to see how people search, but also ‘how they ought to search’. I think that will be an exciting advancement.

What do you think of CERGE-EI?

From what I’ve seen, it’s a beautiful place, and I’m very excited with the faculty here. There are a lot of people who are interested in search and bounded rationality. I’m looking forward to meeting everyone else.

Interviewer: Tamta Bakhtadze

21 January 2013

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The Rich and the Rest: Dr. Martí Mestieri Discusses Income Disparity

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Dr. Martí Mestieri (Toulouse School of Economics) researches the dynamics of new technology adoption in developing countries in order to explore the patterns of convergence and divergence between rich and poor countries. The models he uses show that changes in the pattern of technology diffusion account for at least two thirds of the Great Income Divergence between rich and poor countries since 1800. During his visit to CERGE-EI in December, we sat down with him for a brief chat.

Tell us about the paper you are presenting at CERGE-EI

Before the industrial revolution, the income disparity between countries was not that high. The richest country was maybe twice as rich as the poorest. Now there are much great magnitudes of difference between rich and poor countries. With the industrial revolution, growth has become very uneven. Some countries have experienced sustained growth while others have stagnated. Economists of course have been puzzling with this question for a very long time.

What we do in this project is we try to isolate the channel of technology adoption. Even though there are methods of production that make labor much more productive, it seems that many countries are not adopting the best technologies that are out there. So we collect data for many technologies and many countries and we are trying to understand how much technology adoption (or lack thereof) explains the differences between growth performances in the last 200 years.

This is what they call the ‘Great Divergence’, right?

Exactly. Especially in the 19th century you observed a handful of western countries starting to take off, while others didn’t grow at all. So over 100 years, you develop a huge gap in income per capita.

So how much does this technology adoption channel explain differences?

I wouldn’t say ‘explain’, I would say ‘account’, because we don’t have a fundamental theory for why these differences in technology adoption exist. But we find it accounts for a lot, almost 70%. Indeed the question that lies ahead is what are the drivers of technology adoption. In order to explain this, you have to confront a couple of facts. The first is that adoption lags have been declining over time—poor countries are adopting new technologies relatively faster over time. This means the poor countries are relatively more productive because they are catching up.

But what we find as well is that the rate at which technology diffuses within poor countries is actually slower. So even though they are importing technology faster, they are not diffusing it well within their borders. I think this is a set of interesting facts, and if you want to have a big theory, you need to have a theory that predicts these two things, and this is not entirely obvious.

Can you try to explain some reasons why?

This is the most interesting question, and the one we are after. But just building a consistent methodology to assess differences in technology adoption has been keeping us busy. We have some theories that we are exploring. One idea is that some of the early technologies that were produced in the industrial revolution are important inputs for future technology. We see that emerging economies have been lagging behind because they lack this ‘infrastructure technology.’ We think that the diffusion of new technologies is actually being prevented in these emerging economies because perhaps they don’t have the stock of old technology infrastructure necessary for the new technology. So these lack of old technologies are a bottleneck for the new technologies to diffuse.

Another idea is that income inequality may play a role. This means that new technologies are only adopted by a small and elite fraction of the population, and this fraction is not enough to generate spillover to the wider public. But these are only partial explanations. To get more depth, we have to dig much deeper.

Interviewer: Liyousew G. Borga

7 December 2012

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Talking Politics with Dr. Milan Svolík

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CERGE-EI isn’t just for economists. Dr. Milan Svolík, a political scientist at the University of Illinois,  visited CERGE-EI recently to discuss his research on election outcomes. We sat down with him for a chat:

Tell us about what influenced your decision to become a political scientist.

When I applying for PhD programs, I got accepted to both economics and political science programs. At the time, I was so bored of interest rates and unemployment levels, I thought it would be much more interesting to know why people vote, why we fight wars, why we have some countries that are dictatorships and others that are democracies. So I intentionally decided that I wanted to study political science. That decision was primarily based on the idea that most mainstream economic topics are not as exciting as political topics. I didn’t know at the time that you can actually study most of these topics within the field of economics. Now I know it, but I didn’t at the time!

So what are your research interests?

I study what would be called political economics. I study several things.I’m interested in why some countries are dictatorships and others are democracies. I study transitions to and from democracy. I study the politics of new democracies, why some survive, while others are more or less corrupt or more or less functioning. I also study political accountability—why elections sometimes work as a way of keeping politicians in line, and why they sometimes don’t work.

Why does this research interest you?

I think it has to do with the fact that I’m from Eastern Europe. Because all of these things you can see here. Some countries are dictatorships, some are democracies. Some are more authoritarian, some are more democratic. Why is Russia governed by somebody who looks like a  new dictator, and Czech Republic is not? I think those are basic questions that we should be able to answer, and they have been around for quite a long time.

What insights do you think are the most influential in your work?

I am too young to have produced influential and important things yet (laughs). I don’t think I have any great answers, but I think some of the questions I ask are very very important. If you look at the 20th century, you see there are great economic questions: why and when do we have recessions, why do we have enormous growth in some places and not in others. But I think there are also incredibly interesting political questions. Why are some regimes ‘persistent democracies’ while others are changing from democracy to dictatorship. The changes you can observe in the 20th century provoke as many political questions as economic questions. And many of these questions are poorly understand but deeply fundamental. Why is China still a dictatorship, but managed to quadruple its GDP/cap in the last 20 years? We need to understand what will happen in North Africa. Is being a democracy important for economic growth, or is it ultimately irrelevant? There are so many questions.

How do you think your field will develop and change in the next 10 years?

It’s hard to say. Some of the big questions will remain the same. Even in the politics I’m interested in, the questions are as important today as they were 100 years ago, and we still don’t have completely satisfactory answers. One thing that is changing is the methodology. Very few economists and fewer political scientists were doing experiments even 20 years ago. And now field experiments are becoming very popular. In ten years we will see what we learn from the wave of experimental economics that is being produced right now. I am very interested to see how people digest evidence coming from these experiments.

What do you think of CERGE-EI?

I think it’s an incredible institution. It has an incredible collection of young economists, and these are some of the most talented people from the region. I hope CERGE-EI will be able to keep them here. It’s very hard to find such a number of high quality people in one place.

What advice do you have for new PhD students? 

I would give two types of advice. When I speak to political scientist grad students, I give them the absolute opposite advice as economics students. When I speak to them, I tell them to learn as much technical methodology as they can—to study econometrics, game theory, and formal theory. Because they don’t get enough of it. But for economics students, there is so much technical stuff already forced on you. So for economic students, I would advise them not to forget to always ask, ‘Why? What is the basic economic question here. What is the big question I want to understand?’

The second piece of advice is to be very cautious about ‘fashionable topics’ or ‘fashionable methodology’, because those change very quickly. If something is fashionable in year 1, it may not be fashionable five years later when the student is graduating. I think it’s important to follow gut-feelings about what is important, rather than any particular fashion.

Interviewer: Tamta Bakhtadze

3 December, 2012

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A Brief Chat with Professor Andreas Ortmann

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CERGE-EI discussed lab rats and experimental economics, among other things, with visiting senior lecturer Andreas Ortmann:

You are interested in experimental and behavioral economics. Why did you choose this field and who influenced your decision?

It’s a funny story. To earn my Ph.D., I went to Texas A&M University where one of the first and prominent experimentalists in the US was working. But I didn’t go there to study experimental economics, I went to study industrial organization and public finance. In fact, I never heard about experimental  economics. But …  I needed to earn money and so I started working for this professor called Ray Battalio – a brilliant guy and real mensch – who conducted experiments with animals (I was a rat-lab technician!) and humans. Even though I was very skeptical at first, experimental economics grew on me and I became interested in it as one way of understand economic problems.

But it’s only one way.  It’s not the only way. I also do theory, I do history of thought, and even empirical work if I have to.  I don’t believe in one method alone. You take what’s appropriate to understand and solve the problem .

Today a lot of people are doing these types of experiments. How do you see the field changing in the future?

There are lots of issues that experimental economists need to address. For example, there is always the question of external validity of experiments, i.e., the question to what extent laboratory results tell us anything about the real world.

There are also all kinds of issues of how to do the econometrics, and how to evaluate the evidence that is being produced in experiments. Experimental economists mostly talk about statistical significance, but this is really not that interesting if the effect size turns out to be miniscule. You want to talk about economic significance, which is a different concept. We may actually have the wrong tools for understanding these experiments, at least for some of the big issues which we need to address.

What are your current research interests?

Which of my 273 would you like me to talk about? (laughs) Well I’m working on, among other things, evidence production and evaluation. I’m working obviously on what I talked about in my seminar at CERGE-EI: social-impact bonds, which is a recent addition to  my  research agenda .  I’m also working on simple heuristics. Fast and frugal ways of making decisions which contradict some of the basic ideas that economists have on how we make decisions. We usually assume common and full knowledge and rationality when actors make decisions, and I think the evidence shows that under time-constraints and uncertainty we don’t have a lot of time to make decisions and we don’t have full information. We make decisions on incomplete information, and this is one of the big issues I am focusing on.

How do you get inspiration from your research?

It’s a difficult question because I think there is no algorithm for it. You just have to run through the world with open eyes. And sometimes you have to dive very deeply into a particular topic to understand what kind of open questions there are. Sometimes it just hits you—you say ‘why?’ and then you just go for it.

I think it is fair to say that it has to do, for the most part, with knowing a field well and reading thoroughly. Of course it helps if you read broadly and don’t just focus on one particular topic. And it always is useful to talk and work with different people with different experiences. There are various strategies to get inspirations and they can all be equally valuable.

Do you usually find unexpected results from your studies?

At my age, little surprises me (laughs). But of course, that’s why we do experiments. You do it because you have a theory and you do an experiment to understand if your theory predicts properly. And sometimes the evidence seems to reject the theory that you tested.

Much of laboratory research is being done precisely because theories didn’t predict properly, and then you try to understand what’s going on. You essentially give theorists more grist for the mill to come up with better theories to explain lab results. Of course there are often situations where a theory doesn’t predict well, and it’s a big surprise. Then you have to go on and try to do better. Write better theories, do new experiments. This is the cycle: theory, experiments, theory, evidence, theory, more evidence, more theory, and so on.

You have written many papers, so you may be the right person to ask the burning question of all young PhD students: where do you see the research gap?

You are asking for the low-hanging fruit, yes? (laughs). It’s an interesting question. It’s a little like the famous efficient market argument: there can’t be money on the street, because if there were, someone would already have picked it up. But that’s not true, because there is always new money being dropped, essentially.  There are always new research opportunities. The world is changing and there are always new ideas being generated. In order to understand what’s a new idea, typically it takes some experience and some feeling for what constitutes a good story.

But there are many situations where you come up with something new to the field because you are not caught in this paradigm. So you might see that something is interesting where mainstream economists would never see it. There are so many examples of this.

What is the competitive advantage of CERGE-EI students in choosing a research topic? Since most of the student body is from post-socialist regions, is this the area where they should focus their research?

There shouldn’t be a ‘should’ here. In the first place you must try to become a good economist. After that, if you want to go back to your country, I’m sure you will have plenty of questions and problems to deal with. Just become the best economist you can be, and there will be plenty of opportunities to apply your knowledge.

I can say that good economists are badly needed in the region. Many of the policy decisions are not informed properly by good economics, and that’s a real problem.

How do you perceive the evolution of CERGE-EI?

I think it’s a major success story. It has shown in the region that economics today is very different than the economics practiced here in the past. The way we do and apply economics, and in terms of our ambition, we set a very good example at CERGE-EI. If you look at the rankings of institutions in the Czech Republic, you find CERGE-EI always in the top. I think CERGE-EI has done a lot of good, in the Czech Republic and beyond. You need only look at the career paths of the graduates to see just how successful CERGE-EI has been.

Andreas Ortmann is a professor of experimental and behavioural economics at the Australian Business School at UNSW, Sydney, Australia. He was a professor and senior researcher at CERGE-EI until summer 2009, and he remains affiliated with the Economics Institute of the Academy of Sciences as a visiting senior researcher.

Interviewer: Sophio Khozrevanidz

9 November 2012

 

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Do Women Receive Lighter Prison Sentences?: An Interview with Professor Ronald Oaxaca

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Let’s start in your early years. You finished your BA at California State University. How did you come to study economics?

That’s a good question. I originally planned to major in history and I made an appointment with the chairmen of the Department of History to talk about it, but he happened to be out of town. And I never rescheduled. In the meantime I had a friend who said, “I took a really interesting course called Principles of Economics. You might want to take it.’ And so I took it the next semester and I loved it. It was a discipline that had mathematics and business. It had everything. After that I was captivated.

What motivated you to focus on the gender gap?

I took graduate labor economics and I had an interest in racial discrimination. And I knew a lot of people had done work in that field. But I thought that there had not been a lot of work done on gender discrimination—at least not by economists—and I was right.

So I started thinking about how to apply a model by Gary Becker, that he originally intended to be used to look at racial differences in labor market outcomes, and I decided that that could be applied to research on gender.

So after you graduated, it took you two years to publish your most famous papers, and you got famous for this ‘Oaxaca-Blinder decomposition.’  The decomposition has become so famous that there is now even a Stata command. How did you feel that someone had made this ?

I found out from one of my graduate students. He rushed to my office and said ‘There is a Stata command with your name on it!’. So he downloaded it on my computer, and I was amazed. In fact, it really made doing the work easier!

Beyond the gender gap, what are your other research interests? I see you are going towards crime economics?

There is still a story about decompositions in the paper I presented at CERGE-EI, but instead of looking at labor market outcomes between men and women, this paper looks at differences in prison sentences between men and women. It tries to understand how much of the gap we observe can be explained by circumstances such as women committing less serious crimes, and how much of it is unexplained, and thus may be the contribution of judges’ preferences.

So what are the conclusions from this research?

 We show that, unlike in the labor market, in prison sentences women are in fact favored. There is an unexplained gap. If we look at the majority group, white males and white females, we find that male prison sentences are on average 20 months longer. When you control for the nature of crimes committed, then we can explain 14 months of that gap. But there are still six months that you cannot explain by anything other than judges having a preference in favor of women.

It is very interesting, and one can conjecture as to why that is the case. Some have said, ‘what if judges believe that women learn their lesson faster than men, so then it would be socially inefficient to put them in prison as long as men?’ There are two problems with that idea: one is that we already control for their past criminal history. If they learn their lesson quicker, they wouldn’t have a criminal history as severe as men, so we already controlled for that. The other problem is that even if we really believe that’s true, there is no way you can implement that as part of the legal system. Because where does that end?

Continue reading Do Women Receive Lighter Prison Sentences?: An Interview with Professor Ronald Oaxaca →

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Man Bites Dog – An Interview with Dr. Kristoffer Nimark

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A dog bites a man… no one cares. But how about a man biting a dog? That’s news. Dr. Kristoffer Nimark explores economic events that make news, and how media focus can exacerbate the impact of those events. Dr. Nimark lectured at CERGE-EI this fall about his research, and also sat down for a brief interview. Check it out:   

 Why did you choose to become an economist? 

I started to become interested in economics in the 90s. When I was in high school we had a big economic crisis in Sweden, not unlike the crisis we have now in Spain. And I saw the public debate where people had differing views that were irreconcilable with each other. I wanted to learn economics so I could understand it myself and see which arguments were valid. So I started my undergrad studies in Sweden and later I continued with my PhD at the EUI (The European University Institute) in Florence.

What is your current research?

Mostly my research is about methodological issues about how to model economies where individual agents know different things about things that all the agents care about. So you can think about how a lot of economic decisions depend on the actions of other agents in an economy. For example, if you’re a firm that wants to invest, you would like to know how much your competitors are investing; or if you’re a trader in the stock market or bond market you might be interested in knowing what other traders are willing to pay for a bond at the next trading opportunity.

Once agents have different information, predicting other peoples’ actions becomes more difficult, because you don’t know the same things anymore. And I’m trying to solve these models on how to predict the actions of others. I may want to predict the actions that other agents are taking, and they are trying to predict the actions that I am taking. And since their actions will depend on their expectations of my actions, I need to form predictions about their predictions of my expectations. So we run into methodological issues and try to understand how to solve these dilemma models.

In the paper you presented at CERGE-EI (‘Man-Bites-Dog Business Cycles’) you proposed some information structures which are different from the existing literature on ‘rational inattention.’ Can you tell us about it?

So my paper “Man-Bites-Dog Business Cycles” features one specific feature of news media: unusual events are considered more ‘newsworthy’ than more common events. The title of the paper refers to how when a dog bites a man it is normal, but it is unusual when a man bites a dog. It is only the latter event that will make news.

In the context of the paper, this means that when you have unusual macroeconomic developments like a crisis or a boom, the mainstream media is more likely to focus on the economy. Since the news media is more likely to focus on the economy when we have a recession or a boom, it influences the business cycle. In particular, I show that this intense media focus can exacerbate things—you get stronger booms and recessions than you would otherwise.

Very interesting. What are your main conclusions?

My main finding is that the economy appears to respond strongly to shocks that don’t seem large enough to really justify the types of crises we observe. The model shows how we have sometimes extremely strong responses to only small changes in fundamentals. So even if there is only a small change in productivity, you have a very large change in output, and vice-versa. And this can be partly explained by the intense media focus.

So in this sense, the media focus on the economy is a bad thing?

I don’t show in the paper that this is suboptimal. It’s still possible that this media response is good, that a strong response is appropriate. I don’t really discuss this.

We students are making decisions about our topics for our PhD thesis. Where is the gap in economic research today?

You shouldn’t think too much about it. When you choose a topic as a PhD student you should choose something that you find interesting enough to work on—something you are willing to spend 60-70 hours a week on it for three years. Some people find it’s interesting to work on new things and they’ve identified some gaps in economic thinking they think are important. And I think that’s great, and that’s often very good research. But you shouldn’t artificially look for gaps, especially if you don’t find it interesting—and you won’t be able to convince other people that it’s interesting!

The most important thing is to work on something you find interesting yourself. Most people don’t think it’s interesting to repeat other people’s work, so in one sense it always means plugging some gap somewhere.

Dr. Nimark is a researcher at CREI  (Centre de Recerca en Economia Internacional), adjunct professor at Universitat Pompeu Fabra, and affiliated professor at Barcelona GSE.

Read the paper Man-Bites-Dog Business Cycles

Interviewer: Sophio Khozrevanidze, 2nd Year PhD Student

Friday, 5 October 2012


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Going Once, Going Twice, and Gone! Auction Theory with Professor Paul Milgrom

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 httpv://www.youtube.com/watch?v=bErO0-ujMQg

Fall semester in CERGE –EI was full of interesting events for researchers and students. One of the most memorable is surely the Market Design Conference in October, and in particular the visit of Professor Paul Milgrom from Stanford University.

While serving as a professor in Yale and Northwestern Universities, Paul Milgrom received wide recognition for his revolutionary innovations in practical market design. Currently Dr. Milgrom holds a position as the Shirley and Leonard Ely Professor of Humanities and Sciences in the Department of Economics at Stanford University.

Professor MIlgrom is widely recognized by economists to be the father of the celebrated “linkage principle,” which is extensively used as a valuable strategic tool in market design, particularly multi-unit auctions and procurement. One of Milgrom’s stellar academic contributions was in designing and conducting the first spectrum auctions for US Federal Communication Commission (FCC), which allowed the government to raise enormous amounts of money for spectrum licenses. Modifications of Milgroms’ auction rules for spectrums have been employed all over the world, and now most spectrum licenses are sold through these types of auctions.

At the conference at CERGE-EI, Professor Milgrom gave a public lecture about his recent work for the FCC on the ‘Incentive Auction in the US’. In particular, these types of auctions serve to redistribute efficiently existing 3G licenses. The main challenge in this type of market situation is to achieve satisfaction from both sides: consumers and suppliers (license holders) of 3G services.

During his lecture, Professor Milgrom discussed all possible drawbacks of the ongoing redistribution of the licenses, and showed that one can overcome existing problems by applying “incentive auction” rules. The main intuition is that smaller licenses will be redistributed to bigger providers and hence bigger providers will receive more market power and they will be willing to pay for these licenses. Incentive auction rules develop a unique efficient matching mechanism where first the FCC buys broadcast licenses from providers, and then it repackages them in an efficient way and sells them through the auction again.

The most fascinating thing for CERGE-EI students was to see an immediate application of the economic theory into practice.  Professor Milgrom’s presentation motivated a great deal of discussion on further improvements and modifications on the incentive auction rules. After the lecture, Milgrom kindly agreed to a brief interview, which you can see on our Youtube page. From our side, we want to sincerely thank professor Milgrom for his participation in the conference and his openness to discussing new ideas from CERGE-EI students!

Author: Oksana Oryshchyn

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Nobel Pursuits: CERGE-EI Interviews Nobel Prize Winner Christopher Sims

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Christopher Sims won the Nobel Prize in Economic Sciences in 2011. When he came to CERGE-EI this summer to lecture about his research, we knew we had to sit down with Professor Sims for a more intimate interview. Take a look at the conversation between this brilliant laureate and some of our PhD students at CERGE-EI:

What can you tell us about the journey to winning the Nobel Prize? Where you got your ideas, who influenced you the most, the evolution of the research?

My Nobel Prize is focused on the empirical econometric work I did on monetary policy. My ‘Noble lecture’ kind of goes through that, but I can summarize it: In the 1950s and early 1960s, Keynesian Economics was dominant. And in the 50s, which was very close to the Great Depression, the Keynesian consensus was that monetary policy was not very important and fiscal policy was. And this was a legacy of the period of the Liquidity Trap in the 1930s, when monetary policy indeed was not very effective—a period much like the present, in that respect.

Back then, econometricians developed large statistical models, based on Keysian Theory. They built on the insights of Trygve Haavelmo and Jan Tinbergen, two earlier Nobel Prize winners. And they ended up with very large unwieldy models, for which the statistical methods proposed by Haavelmo didn’t really work very well.

So into this scene came the monetarists led by Milton Friedman, and they used much simpler statistical models and focused on just a few variables. They argued that the connection between the money stock and income was the central, most important fact in macroeconomics. But this factor didn’t emerge as central and important from the perspective of those big Keynesian models.

There was really no way for these two schools to resolve their differences with the econometric methods that were available at the time. But in the big Keynesian models, everyone knew they made assumptions that weren’t believable.

So what I did was first I validated the monetarists. They were running regressions of nominal GDP on current and past money stock, and interpreting them as policy-exploitable relationships. They interpreted them as if changing the money stock would change nominal GDP according to the coefficients they estimated in those models. I argued that if that were true, there was a testable implication. This was a causal model. In this logic, future money should not be correlated with income, given past money. So I checked that implication and it turned out that the implication was satisfied by the data.

And so something that both Keynesians and I would have predicted would show that the monetarists were wrong, actually showed they were probably right.

But then a student of mine, Yash Mehra, did a study of so-called ‘Money Demand Equations’. Because at the same time that Friedman was estimating these income-on-money regressions, other people were putting money on the left side and income and interest rates on the right. They were calling this ‘money demand’, and it was another similar equation regression. So I said to Mehra: ‘this looks like a good thing to check, because If money is causing income, than these money demand equations must be nonsense. Money doesn’t belong on the left-hand side.’

But Mahra did the tests and it turned out they passed. He showed that with money on the left and income and interest rates on the right, it looked like everything on the right-hand side satisfied this condition that the future size of the variables shouldn’t matter.

I was puzzled by these results and decided that I wasn’t going to make sense of them unless I put together a model with more than one equation. So I estimated a small, vector regression with several equations, and once I did that I could see that interest rates predict money, and if that’s right, then the usual ‘monetarist’ interpretation of this system didn’t really hold up.

So there is a kind of consensus now on how the economy dynamically responds to monetary expansion or tightening. It’s not really precise, but GDP tends to respond a little quicker than prices, and they both tend to go down when money is tightened. These come right out of quantitative statistical estimates, and there are different ways to do the identification, to separate these two influences. And they give consistent results. That was what the prize was for, that sequence of developments.

Continue reading Nobel Pursuits: CERGE-EI Interviews Nobel Prize Winner Christopher Sims →

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