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Some thoughts by Prof. Hogarth on Prof. Thaler’s award of the Nobel Prize in economics

December 7, 2017

llustration: Niklas Elmehed. Copyright: Nobel Media AB 2017


By Orestis Vravosinos (Economics ’18)


Universitat Pompeu Fabra and BGSE Emeritus Research Professor Robin M. Hogarth shares some thoughts in the light of Prof. Thaler’s award of the Nobel Prize in economics.

 

  1. What would you deem to be the most significant contributions of Prof. Thaler?

Prof. Hogarth: “I’m very glad that Thaler got the Nobel Prize, because it’s a recognition that the field of behavioral economics has to be taken seriously. He has made the field more popular and two of his books are quite interesting. Even though from an academic point of view ‘Nudge’ may not be so strong, in both ‘Nudge’ and ‘Misbehaving’ Thaler has done a very good job in explaining things and making behavioral economics accessible to the wider public. ‘Nudge’ has spurred the creation of nudge initiatives in the UK and the US.”

Prof. Thaler has greatly contributed in popularizing behavioral economics and manifesting the insight it can offer in practice and policy, especially though the use of nudges bringing nudge theory to prominence. However, as is most times the case with influential research, Prof. Thaler’s work has also been a matter of controversy, as criticism of what is called ‘libertarian paternalism’ has developed. Concerns have been raised both in regards to freedom of choice (e.g. Mitchell, 2005; Veetil, 2011) and the efficiency or optimality of paternalistic policies (e.g. Rachlinski, 2003; Mitchell, 2005; Glaeser, 2006).

  1. How do you think concerns regarding the use of nudges can be alleviated? Do you think there needs to be any form of regulation on it?

Prof. Hogarth: “Thaler and his co-authors have supported nudge coining the term ‘libertarian paternalism’. Although I don’t think the term makes much sense, I don’t see what is wrong with governments saying that some things are better for people than others; advertisers do it all the time.

In some EU countries there is a total rejection of organ donation after death, while in others almost total acceptance and the reason is the difference in the default option on the driving license among countries. I don’t see why this is wrong; since a default has to be chosen, why not choose the one that is on average better for everybody? Provided that people can still go against the default, if they want to. Governments should be able to use as much social science knowledge as they want. As long as there is “good knowledge”, why should we ignore it? Whether it comes from sociology, psychology, anthropology, economics or whatever, we should use it, as long as it is good for the society.

I don’t think there is any need for regulation of nudge. One should not regulate how advertisers advertise the products, as long as they say the truth. Similarly, governments or agencies should be allowed to design choice; they just need to be honest and clear about it.”

 

  1. Are there any other thoughts you would like to share in the light of this year’s awarding of the Nobel Prize in Economics?

Prof. Hogarth: “Another interesting aspect of Thaler’s work is that it has managed to make an impact without being heavily mathematical. The other point I would like to make is that Thaler owes a tremendous debt to Tversky and Kahneman. Prospect theory provided a framework for explaining things Thaler thought of.”

 

We kindly thank Prof. Hogarth for sharing these thoughts with us.

References

Glaeser, E. L. (2006). Paternalism and Psychology. The University of Chicago Law Review, 73(1):133-156

Mitchell, G. (2005). Libertarian Paternalism Is an Oxymoron. Northwestern University Law Review, 99(3):1245-1277.

Rachlinski, Jeffrey J. (2003). The Uncertain Psychological Case for Paternalism. Northwestern University Law Review, 93(3):1165-1225.

Veetil, V.P. (2011). Libertarian paternalism is an oxymoron: an essay in defence of liberty. European Journal of Law and Economics, 31: 321-334.

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Financial Knowledge and Behavioral Economics as Means to a Wealthier Life

November 30, 2017

Photo credit: Nattanan Kanchanaprat


By Orestis Vravosinos (Economics ’18) – This article first appeared on the blog of Nudge Unit Greece.


In recent decades, financial literacy has been gaining more and more research interest with experts emphasizing that it can be conducive to more efficient financial decision-making. Given the tough economic situation in many countries around the world after the global financial crisis, the need for financial literacy has become even more imperative. Through financial literacy people can both achieve higher levels of wealth and better allocate it in order to make the most out of it or, as an economist would say, maximize their utility.

Before we start to examine the ways, in which financial literacy can inform our decisions, we first need to define financial literacy. An integrative definition stressing both financial knowledge and the ability to put it in practice has been proposed by Remund (2010, p. 284), who defines it as

a measure of the degree to which one understands key financial concepts and possesses the ability and confidence to manage personal finances through appropriate, short-term decision-making and sound, long-range financial planning, while mindful of life events and changing economic conditions.

Financial literacy and behavioral economics enhancing financial decision-making

Financial knowledge can enhance decision-making by raising awareness about some common behavioral errors people are susceptible to, when making financial decisions. Estelami (2009) argues that financial literacy programs could fight typical financial decision-making errors, such as hyperbolic discounting, short-term memory overload, anchoring effects, inaccurate risk perceptions and mental accounting. Similarly, Loerwald and Stemmann (2016) suggest that, when people become aware of some common human decision-making errors, they can better resist to making them, while the importance of financial education and financial literacy is also stressed by Altman (2012) and Shen (2014), who addresses overconfidenceanchoring and framing effects.

Another major mistake people do is that they often hold under-diversified portfolios. The importance of portfolio diversification in mitigating risk and achieving optimal return and risk combinations has long been acknowledged both in academia and by investment professionals. This crucial role of portfolio diversification has become even better acknowledged, since Harry Markowitz’s –recipient of The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in 1990– seminal paper Portfolio Selection(1952)*. However, studies have shown that many households and individual investors hold under-diversified portfolios.

Fortunately, based on empirical evidence it appears that individuals with higher financial knowledge possess better diversified portfolios (Goetzmann and Kumar, 2008; Calvet, Campbell and Sodini, 2007; Guiso and Jiappelli, 2009; Abreu and Mendes, 2010; Kimball and Shumway, 2010).

Struggling against bad financial decisions

Nevertheless, as Estelami (2009) underlines, knowledge of financial matters cannot guarantee success in our financial decisions, as behavioral errors and biases in these decisions are often found to affect even the most financially knowledgeable. The father of Modern Portfolio Theory, whom we met just above, Harry Markowitz, admitted that he had used the 1/N heuristics or naive diversification; that is, he simply assigned equal weights to all assets in his portfolio without for example looking at the correlations among the included assets. He attributed this decision to regret aversion“My intention was to minimize my future regret, so I split my retirement plan contributions 50/50 between bonds and equities.” (Mitra, 2003; Pompian, 2012).

What’s the takeaway?

Financial literacy and knowledge of our psychological and cognitive biases and errors are key factors that can help us lead a wealthier life. However, the battle against bad financial decisions is not a piece of cake for any of us. The best we can do is to start this learning journey in financial literacy and behavioral economics -which hopefully you have already done by clicking on these eye-catching hyperlinks in the text. That way, the next time we are buying a new 20.000€ car and are presented with these gorgeous 2.000€ accessories to buy with (because “come on, it’s peanuts, I’m already spending 20.000€ on the car”), we will know that we may be falling for mental accounting and “bundling”. Therefore, we need to think twice if we value these accessories that much; maybe these 2.000€ spent on something else would finally prove to be much more useful and make us a lot happier!

 

Note: The idea for the last example comes from the lecture Mental Accounting and Expenditures of the free online course Behavioral Finance, which you may well want to enjoy by clicking here.

*The concept of diversification had been known for many years before, but Markowitz’s work provided a solid theoretical framework and helped lay the foundations of Modern Portfolio Theory. For an assessment of the early history of portfolio theory, see Markowitz (1999).

References

Altman, M. (2012). Implications of behavioural economics for financial literacy and public policy. The Journal of Socio-Economics, 41(5), pp.677-690.

Estelami, H. (2009). Cognitive drivers of suboptimal financial decisions: Implications for financial literacy campaigns. Journal of Financial Services Marketing, 13(4), pp.273-283.

Loerwald, D. and Stemmann, A. (2016). Behavioral Finance and Financial Literacy: Educational Implications of Biases in Financial Decision Making. In: C. Aprea, E. Wuttke, K. Breuer, N. Koh, P. Davies, B. Greimel-Fuhrmann and J. Lopus, ed., International Handbook of Financial Literacy, 1st ed. Springer Singapore, pp.25-38.

Markowitz, H. (1999). The Early History of Portfolio Theory: 1600-1960. Financial Analysts Journal, 55(4), 5-16.

Rasiel, E. & Forlines, J. (2016). Mental Accounting and Expenditures. Lecture, Behavioral Finance by Duke University on coursera.org.

Shen, N. (2014). Consumer rationality/irrationality and financial literacy in the credit card market: Implications from an integrative review. Journal of Financial Services Marketing, 19(1), pp.29-42.

And finally, the paper we all have been waiting for: “Death by Pokémon GO“.

November 22, 2017

Mara Faccio (Purdue University, NBER, ABFER, ECGI) and John J. McConnell (Purdue University) released a working paper this month which will definitely cause some stir with the general public. One thing is sure already: it made it onto our list of the most entertaining economics papers released this year. Titled “Death by Pokémon GO” it uses an event-study design to estimate the total incremental cost of playing Pokémon GO while driving in Tippecanoe County, Indiana.

Though the paper may seem funny at first, it does touch on some serious issues. It links the widespread use of smartphones and increases in app usage to increased car crashes and fatalities. The authors state that: “[…] [T]he possible connection between smartphone usage and vehicular crashes has been cited by the Insurance Information Institute as one explanation for the 16% increase in insurance premiums between 2011 and 2016.“ Faccio and McConnell also note that: “Attributing any increase in crashes and fatalities to smartphone usage and app availability is, of course, extraordinarily difficult given that many other factors also changed over the years in which both increased.”

Although not being the first to investigate the connection between the rise of the smartphones and vehicular crashes, Faccio and McConnell provide some novel insights by making use of an ingenious idea and providing robust results. By employing a difference-in-difference analysis that controls for a variety of confounding factors, Faccio and McConnell can show that crashes near PokéStops significantly increased from before to after July 6th, 2016 (when the game was released). The authors find that the costs associated with this increase in vehicular crashes range from $5.2 million to $25.5 million1 over the first 148 days following the release of the game. Extrapolation of these estimates to nation-wide levels yields a total cost ranging from $2.0 to $7.3 billion for the same period.

The paper is available from the SSRN website: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3073723


1. With the variability in the range being largely attributable to the sad loss of two lives.

BGSE Data Talks: Professor Piotr Zwiernik

November 15, 2017

The Barcelona GSE Data Science student blog has a new post featuring an interview with Piotr Zwiernik (UPF and BGSE), Data Science researcher and professor in the BGSE Data Science Master’s Program:

Hello and welcome to the second edition of the „Data Talks“ segment of the Data Science student blog. Today we have the honor to interview Piotr Zwiernik, who is assistant professor at Universitat Pompeu Fabra. Professor Zwiernik was recently awarded the Beatriu de Pinós grant from the Catalan Agency for Management of University and Research Grants. In the Data Science Master’s Program he teaches the maths brush-up and the convex optimization part of the first term class „Deterministic Models and Optimization“. Furthermore, he is one of the leading researchers in the field of Gaussian Graphical Models and algebraic statistics. We discuss his personal path, the fascination for algebraic statistic as well as the epistemological question of low-dimensional structures in nature…

Read the full interview on the Barcelona GSE Data Scientists blog

Five lessons from a one-week meeting with 18 Nobel Laureates

October 25, 2017

Photo credit: Lindau Nobel Laureate Meeting


By Fernando Fernández (Economics ’13, GPEFM) [1]


“Just when we thought we had all the answers, all the questions changed.” Mario Benedetti

That was my reaction when the 6th Lindau Meeting in Economic Sciences concluded. This meeting occurs every two years and gathers several Nobel Laureates and young economists (graduate students and assistant professors) from around the world. This meeting is certainly the most inspiring academic event I have ever attended.

The meeting took place in the beautiful town of Lindau, next to Lake Constance, in southern Germany between August 22nd and August 27th. During these days, we attended lectures from 18 Nobel laureates in Economics on a wide range of topics: bounded rationality, investment management, pension design, monetary policy, labor markets, morality and markets, political systems, innovation, and econometrics. I will not attempt to summarize these great lectures but all of them were recorded and are available on this link.

 

I would rather focus these lines on the interactions that occurred outside the “classroom”. Every day the program included lectures, lunch, seminar presentation panel discussions, and dinner.

The first lecture was given by Daniel McFadden [2], and besides the content, something really caught my attention. In the first row of the room (it was actually a theater) you could see the other Nobel Laureates. All were carefully listening to the speaker! They seemed like young students paying attention to an important professor. So the first lesson from this meeting was that we, as researchers, should actively embrace our academic curiosity.

Over lunch, I had the first opportunity to talk to a Nobel Laureate. I was sitting with some friends I just met and were talking about each others’ research. At some point, Bengt Holmstrom asked: “Would you mind if I join you?” We welcomed him, and seconds later he started asking us about our research interests. He soon realized that all of us were doing empirical work and said: “I am the only theorist in this table!”

He listened to all of us, asked some questions (some of them were hard to answer) and even gave us some advice. I was able to confirm that these brilliant economists have a special talent to listen to others, even if they are PhD students struggling with their papers. He was very generous with his time and recommended us to work hard but only on topics that we really cared about. He also advised us not to focus on publishing papers but instead on gaining respect from our peers through our work.

Hours later, I had the chance to sit on the table with Eric Maskin for dinner. He told us about the day he received the call from Stockholm and found out he won the Nobel prize. Then, we talked about US politics, big data, increasing co-authorship in economic journals, and other current issues in academia. As you can imagine, when you are sitting next to a Nobel Laureate you get the feeling that you can ask him any question. Well, these questions (some of them unrelated to economics) arrived and Maskin, very modestly, said : “I know very little about this particular topic, so I cannot have an informed opinion. In fact, you should know that one wins the Nobel prize, not because you know everything, but because you specialize in certain specific topics”. His reaction really impressed me but he was right. He could not be an expert in every topic and he acknowledged it. How many times do we feel the need to have an opinion on everything? The second lesson from this meeting is that we must always acknowledge our limitations and be humble enough to don’t give uninformed opinions.

One of the big questions most PhD students have is the following: where do great ideas come from? Tirole, Hart and Holmstrom provided some light on this issue and their advice was the third lesson. Tirole said two great sources of ideas were talking to people around you (his office was next to Hart’s) and to people outside the academia (practitioners, policy makers and business men). He encouraged us to talk to practitioners because they are facing the real problems we must address, that they have many important questions that remained unanswered and deserve our attention. Holmstrom said that the idea of his well-known model of career concerns (one of the reasons he was awarded with the Nobel prize) came when he has working in a plant in Finland, and had some problems with his manager. He then went to do his PhD and wrote a model to explain the behavior of this manager. In addition, he recommended us to become experts in the literature of our field of interest, not to follow it but to depart from it. After this, Hart said that working with Holmstrom and Tirole was a great way to find ideas. He also suggested us that when doing theoretical work, we should keep models as simple as possible.

James Heckman’s lecture was about the identification problem in econometrics. He was the most enthusiastic person I have ever seen giving an econometrics lecture. And this enthusiasm was quite contagious. Even though he was talking about highly technical and complex conditions for a new interpretation of Instrumental Variable (IV) estimates, I was surprisingly able to follow his lecture and understand the contribution he was making. Or, at least that’s the impression I had. That same day, we had a Bavarian dinner at night, with traditional music, food, and of course, beer. This was the last night of the event and the time to say good-bye to other fellow economists.

The coolest table at the Bavarian dinner

After some drinks, I decided to walk back to my hotel, located around 50-minutes away from the place we had dinner. On my way, I ran into Heckman, who seemed a bit confused. He had been walking with other young economists and then he was not sure where to go. I approached him and we realized we had to walk in the same direction. This was quite a unique and unexpected opportunity to talk about his lecture. So I started with my questions and he replied to all of them with great patience and enthusiasm. I could confirmed I had actually understood his lecture. Then, we started talking about the rapid increase in data availability and how big data should influence econometrics. He also told me good stories about his last trip to Barcelona and Peru. Eventually, we arrived at the hotel and said good-bye. This great conversation was the fourth lesson: we should remain enthusiastic even after years of dealing (doing research or teaching) with the same subject.

The fifth lesson is that these people seem very happy doing their jobs. Yes, I know, they are Nobel Laureates, they have already accomplished important professional goals. But it is still surprising how much they enjoy doing research. During lunch time or dinner, when we were able to talk to them more informally, people would usually ask: Which are the questions we should tackle? What fields are relevant now? Most Nobel Laureates seemed to share the view that the relevant questions are the ones you really care about. And if they actually work according to this view, it is not that hard to understand why they look like if they were having fun all the time.

When I was heading to this meeting, I had a lot of questions in my mind and thought the meeting would be an ideal place to get answers. During the meeting, some of my questions were being answered but later I realized that getting answers was not so important. Once the meeting was over, I realized all the lessons I took from it were unexpected. I had misunderstood the purpose of this meeting. I should have not come to the meeting looking for answers. I should have come looking for questions. These highly talented economists are Nobel Laureates precisely because they are extremely good at raising questions. Questions that open new streams of work. Questions that people had overlooked but that deserve careful thinking and attention. Now, two months after the meeting, I realize that all the questions raised by these Nobel Laureates are the reason why this event was so inspiring. Because in research that’s what keeps us working: Questions!


[1] I am thankful to the Marie Sklodowska-Curie Fellowship (through the PODER network) for sponsoring my participation in the meeting.

[2] Before McFadden’s lecture, there was a keynote address by Mario Draghi, president of the European Central Bank.

BGSE represented by “Just Peanuts” at Data Science Game finals in Paris

October 24, 2017

Class of 2017 Data Science graduates Roger Garriga, Javier Mas, Saurav Poudel, and Jonas Paul Westermann qualified for the final round of the Data Science Game in Paris this fall. Here is their account of the event.


Data Science Game is an annual competition organized by an association of volunteers from France. After competing in a tough online classificatory phase during the master we classified to the finals in Paris where we would be presented with a new problem to solve in a 2 days hackathon.

The hackathon was held in a palace property of Capgemini called Les Fontaines. It was an amazing building that made the experience even better.

The problem presented was to estimate the demand of 1.500 different products on 4 different countries using historic orders from 100.000 customers during the past 5 years by forecasting the three subsequent months. This was a well defined challenge that could be tackled with a large variety of solutions and for us specially the time constrain was one of the main challenges, since at the end we could be only 3 instead of 4.

We started by exploring the data and we realised that there were a lot of missing values due to a cross of databases done by the company who provided the data. So we spent some time by cleaning up the data and filling some of the missing values, to later on apply our models. After all the cleaning the key element to solve the challenge was later on to engineer good features that would represent well the data and then apply a simple model to predict the 3 months ahead.

The hackathon can be summed up in a day and a half coding, modeling and discussing without sleeping surrounded by 76 other participants from all across the world that were basically doing exactly the same, with short pauses to eat pizza, hamburgers and Indian food. So, a pretty good way to spend a weekend.

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Why more educated individuals are not always healthier

October 19, 2017

BGSE Voice

Caleb Hia ’18 wrote the following article on health economics from his research for his undergraduate dissertation at the University of Edinburgh.


From 2006 to 2007, almost half of the UK’s National Health Service’s (NHS) costs were attributed to behavioural risk factors: diet-related sickness, sedentary lifestyles, smoking, alcohol and obesity cost more than £15 billion (Scarborough et al., 2011). This mammoth sum, deemed an economic burden on public resources, attracted the government’s attention. In the recent Budget, the Chancellor introduced a tax on the sugar content of soft drinks from 2018 to tackle childhood obesity aimed at compelling individuals to consider external costs associated with its consumption which they do not bear such as the publicly-funded health costs of treating diet-related diseases. The effectiveness of this or any further government intervention in an attempt to correct this “externality” will influence the way the NHS allocates its limited resources in healthcare provision.

Beyond this political issue runs an underlying discussion of the social determinants of health which have long been studied (Wilkinson and Marmot, 2003; Adams et al., 2003). In particular, the effects of education on health has been of interest since the inception of Grossman’s (1972) health model. Grossman’s model suggests health can be maintained by health investments, depending on goods and activity consumption, which affect health although health depreciates as individuals age. As better health gives an individual more time to work and enjoy consumption, more educated individuals are expected to demand more health and invest more in their health. This implies more educated individuals are also more efficient health producers.

A possible causal link between education and health exists possibly because higher productivity from more education directly translates to a higher level of health production through allocative efficiency (Kenkel, 1991; Rosenzweig, 1995) and productive efficiency (Grossman, 1972). For example, low literacy is associated with a poor understanding of hospitals’ discharge instructions (Spandorfer et al., 1995) while higher educated individuals are more likely to follow medical treatments (Goldman and Smith, 2002). Relatedly, higher educated people spend more time on health-related activities because they are better at allocating inputs (Grossman, 1972). Additionally, higher educated individuals use their higher earnings to purchase healthier lifestyles (Glied and Lleras-Muney, 2003) which entail more expensive medical treatments, healthier food consumption and living in healthier areas.

I use a natural experiment in England, the increase in compulsory schooling laws from fifteen to sixteen years old following the Raising of School Leaving Age Order in 1972, and an instrumental variable (IV) regression model to examine the relationship between education and health in greater detail. My sample incorporates additional years of data from Health Survey England between 1991 and 1993 which were not analysed before. I measure various health-related measures and behaviours including Body Mass Index (BMI) which has not been considered before. I run Ordinary Least Squares (OLS) and two-stage least squares (2SLS) regressions in a sample containing all individuals and a discontinuity sample comprising individuals born only in January and February using February-born individuals as my instrument. I show education has no causal effect on various health-related measures and behaviours.

A possible explanation for this lies in time inconsistent preferences supported by behavioural economics. Quasi-hyperbolic discounting (Phelps and Pollak, 1968; Laibson, 1997) induces dynamically inconsistent preferences contrary to geometric discounting. The following payoff matrices models a hypothetical situation where an individual fails to quit smoking due to quasi-hyperbolic discounting:

Under geometric discounting where ∝ ≈ 1 and β ≈ 0.8,

he makes time consistent choices regardless of when benefits to those choices are delayed. Since he gets more utility from quitting in both periods, he quits immediately.

However, under Quasi-hyperbolic discounting where ∝ ≈ 1 and β ≈ 0.8,

he changes his choices based on his distance in the future. Unlike geometric discounting, he gets more utility from quitting only in future and not at present and hence do not quit.

The empirical evidence from Gruber and Köszegi’s (2001) addictive behaviour model which incorporates time-inconsistent preferences to the standard “rational addiction” model (Becker et al., 1994) suggests smokers exhibit forward-looking behaviour with time inconsistent preferences concerning smoking. Thus, individuals start smoking often as adolescents when they are most present biased (Hammond, 2005) and do not anticipate the difficulty of quitting.

Therefore, lifestyle habits may not be correlated with education. In the case of smoking, individuals who quit smoking successfully may have used commitment devices (Ashraf et al., 2006; Kaur et al., 2010; Beshears et al., 2011) like quitting with friends to constrain their own future choices by deciding ahead of time to make future deviations costly. Increasing the education budget may be a sound way to promote public health but understanding behaviours and exploring policies to incentivise individuals to adopt healthy habits may be more effective in the long-run.

Download the full paper:

The causal relationship between education and health-related measures and behaviours: Evidence from England