Wednesday, September 30, 2015

Lecture on Rationality, Utility, Value and Decision Making

I am currently giving a set of lectures as part of a module "Behavioural Economic: Concepts and Theories" in Stirling. I am posting brief informal summaries of some of these lectures on the blog to generate discussion.

Fig 1. Overview
Today's lecture was on Rationality, Utility, Value and Decision-making. The lecture consisted of six sections (Fig. 1): (i) concepts of rationality; (ii) rational choice in conditions of certainty; (iii) rational choice in conditions in conditions of uncertainty; (iv) challenges to rational choice (v) loss aversion and the endowment effect; and (vi) implications of rationality assumptions and threats to their validity for policy. 

(i) Concepts of rationality
The main point of this lecture is to give a working definition of what we mean by rationality in Economics. This is a complex construct with many potential meanings across a wide range of literatures. In Economics we generally tend to mean that decision makers are consistent in their behaviour rather than to question their motivations. The basic microeconomic models of the consumer generally assume rational utility maximising behaviour.

(ii) Rational choice in conditions of certainty 
In the simplest case of choice under certainty, consumers are assumed to be able to represent all alternatives, rank them consistently and choose the bundle of goods they prefer the most subject to the constraints that they face. Rational consumers allocate their time to work and leisure and the subsequent income to savings and consumption so as to maximise their life-time utility. The implicit or explicit ability to perform the computations necessary to enact optimal behaviour underlie models of choice of consumer goods, labour supply and saving.

In conditions of uncertainty, the models so far assume that people are able to represent accurately uncertain outcomes efficiently using available information and to choose consistently between alternatives with uncertain outcomes. If people behave in this fashion and markets are open, then we can view their behaviour as revealing their preferences and we can also predict how they will respond to changes in price and other constraints and the effects of these changes on their welfare. We will revise the basic models in the lecture.

Rational decision makers should obey the axioms of (1) completeness i.e. they should consider all possible alternatives and have defined preferences for all alternatives (2) transitivity i.e they should be consistent in their preferences so that if A is preferred to B and B to C then A is preferred to C (3) Diminishing Marginal Utility and Diminishing Rate of Substitution i.e. as the person acquires more of a given good their marginal value of it becomes less relative to other goods (4) Non-Satiation i.e. people do not have so much of everything that they do not want any more (5) Reflexivity - a technical assumption which means that A is worth A.

These conditions define people's preferences. If people hold these preferences, they will make choices that are rational provided they have the full information and there is no external obstacle to making their choices. People make these choices subject to the constraints that they face. The main constraints they face are the endowment of wealth and talent they bring into the world, the prices of goods, the wages that they can acquire from working and interest rates. Rational economic actors maximize their well-being (utility) by choosing how much to work at the given wage rate; choosing how much to save in different savings and investment vehicles and choosing the bundle of current consumption goods that they prefer they most from all the available alternatives. 

(iii) Rational choice in conditions in conditions of uncertainty
Under conditions of uncertainty, rational individuals must be able to attach accurate probabilities to all potential outcomes arising from different decisions. They then must attach value to each of these probabilistic outcomes. They must also attach a value to the risk itself, with different people being risk averse, risk neutral or risk loving.

Fig 2. A Rational Gamble
The mathematical model of how people attach value to probabilistic outcomes is known as a Von-Neumann Morgenstern utility function. It simply says that people multiply the subjective of an outcome by the probability that it will occur following an action (see Fig. 2 for an example).

Rational individuals try to live their lives by maximising the subjective expected utility arising from all their behaviours. If people behave in this fashion, we can say that their behaviour is the best measure of their welfare - this is called "revealed preference" in economics. Similarly, we can derive the value of goods by looking at how rational people choose between them. The value of something is the rate at which rational people trade the item off against other items - in general in Economics, we choose money as a comparison good and express the value of goods in terms of currency.

We also assume that economic agents care only for their own welfare and act to maximise their own individual utility. Technically, this is a separate assumption from rationality as one can be rational and altruistic or conversely irrational and greedy. In general, we will look at rationality and altruism separately. Furthermore, there is no real separation between the types of decisions that are of interest to economists and those that are not. When we use phrases such as consumption, saving, leisure, investment etc., we are referring to a very broad range of phenomena. Throughout the course, I will use examples from behavioural game theory rather than simple consumption examples as I think these illustrate the real-world importance of these issues. For now, you need to get a working definition of rationality something similar to the above into your mind so that you have a framework for what follows.

(iv) Challenges to rational choice 
Fig 3. The Allais Paradox
We examined early challenges to the formal model of rational choice, in particular the Allais and Ellsberg paradoxes. The Allais paradox is relatively simple. In Figure 3, look at the gambles and decide which one you would choose.

The "paradox" is that most people exhibit preference reversals in an expected utility sense in that they often choose 1A and 2B. If you look at the expected value of the gambles (see Fig.2, just multiply the value by the probability of receiving it) a person with consistent preferences would choose either 1A and 2A or 1B and 2B - after all the B gambles are essentially the same as the A gambles, we have just added an 80% chance of receiving 0 to both. In reality we often see preference reversals because many people have a preference for certainty and/or are motivated by regret aversion in the case of 1A.

Fig 4. The Ellsberg Paradox
The Ellsberg paradox (Fig. 4) is from one of the most cited papers in behavioural economics and is a little more complex. Look at gambles 1 and 2 and decide which ones you would choose.

We know 30 of the 90 balls are red (so a 1/3 chance of drawing red) and 60 are black or yellow, but we can't calculate probabilities for them because we don't know the distribution. It could be 1 black & 59 yellow, 30-30, 59 black & 1 yellow or any other combination. In economics terminology this is is a case of uncertainty (where probabilities are not known) rather than risk (where they are). All we can say for sure is that there's a 1/3 chance of drawing red and a 2/3 chance of not drawing red. Most people choose 1B here. Assuming a person is not just picking randomly, then if they pick 1B we may assume it's because they have deemed the probability of red appearing as greater than that of black appearing, i.e. they think p(black) < 0.33 since we know p(red) = 0.33. In other words we think there are 29 black balls or less.

Gamble 2 is identical to gamble 1 except we now add the yellow balls for both A and B choices. Since we already preferred red to black last time, adding an equal amount of yellow balls to both sides shouldn't matter. If red > black, then red + yellow > black + yellow. The paradox here is that most people, having chosen 1B, now choose 2A. Why is that? Essentially it's because most people display ambiguity aversion. Black + yellow has known risks; there is a 2/3 (66%) chance of winning and a 1/3 chance of failure. Red + yellow is uncertain; the chances of winning could be 34.4% (if there's 1 yellow) or 98.8% (if there are 59). Your choice here will depend on how much variance you are willing to tolerate.

In addition to these paradoxes, Rabin (2003) gives a thorough but accessible discussion of the main tenets of rational choice in economics and the potential problems with these assumptions. Much of the rest of the course will evaluate the evidence on how people make decisions and how this compares with the basic textbook model.

It is worth pointing out at this stage that the rationality assumptions in Economics at first appear ridiculous. We know that people do not perform billions of explicit calculations each time they choose a product. However, it should be kept in mind that most accounts of rationality do not need to assume that they do. Instead, many economists believe that the markets contain sufficient cues to allow people to act rationally even if they cannot perform the computations explicitly or that sub-optimal behaviour will simply not survive in a competitive market. I might decide tomorrow to set up a business exporting sand to North Africa but I will quickly find out that this is not a sensible thing to do or else I will just go broke. When we are evaluating the rationality postulates it will be important to push them to their limits. The ultimate test will be whether groups of people systematically act inconsistently in important areas of their life in a persistent fashion. We will go through Rabin's account in the lecture and form an initial impression of the overall argument. 

(v) Loss aversion and the endowment effect:  
The fifth part of the lecture introduced the ideas of loss aversion and the endowment effect. The basic idea of losses relative to a reference point being valued more than gains was developed by Kahneman and Tversky (1979) and has had a major influence on economics and related fields. See Fig. 3 to see the main insight of their Prospect Theory visually; note than a gain of 1 unit causes 1 extra utility, whereas a loss of 1 unit results in a disutility of almost 1.5. This is a simple example but gets at the idea that people asymmetrically value losses and gains.
Fig 5. Prospect Theory and Loss Aversion

Fig 6. The Endowment Effect
We examined experimental evidence on the endowment effect, in particular the famous mugs experiment of Kahneman et al, demonstrating that experimental subjects assigned to owning and selling mugs valued them more highly than those assigned to purchase them. To explain the experiment briefly, there were 3 groups: (1) buyers, who got some money and were asked how much they were willing to pay (WTP) for the mug, (2) sellers, who got a mug and were asked how much money they were willing to accept (WTA) to give it up and (3) choosers, who could choose the mug or say how much money they were willing to accept instead. The results (Fig. 6) are striking; the sellers demanded about twice as much to give up the mug as the buyers were willing to pay. The choosers, who were not subject to the endowment effect because they were not in physical possession of the mug, were willing to pay about the same as the buyers.

There is now a massive literature on the endowment effect and loss aversion that we will review later in the term. 

(vi) Implications of rationality assumptions and threats to their validity for policy
Beshears et al (2008) is an interesting and accessible account of why people's behaviour may not be fully rational in the sense used in the textbook. Beshears et al argue that many choices are characterized by conditions where the chooser does not have much experience, where third-party pressures are operant, where the chooser does not have much scope for trial-and-error and where starting points and consumer inertia dominate active choice. In such conditions, there may be a big gap between what people choose and what they would choose were they making fully informed and deliberative choices. This is an enormous challenge to basic economic theory and also potentially has major policy implications. 

1. All mainstream microeconomics undergraduate textbooks (such as Varian) contain relevant sections on consumption, savings, investment, labour supply, choice under uncertainty and game theory. 
2. Chapter 3 of Wilkinson and Klaes "An Introduction to Behavioral Economics" contains a useful overview of the main concepts in utility theory. 
3. Beshears et al. (2008), "How are preferences revealed?," Journal of Public Economics
4. Rabin (2002), "A perspective on psychology and economics," European Economic Review

Monday, September 28, 2015

Education, Gender, and State-Level Disparities in the Health of Older Indians: Evidence from Biomarker Data

Education, Gender, and State-Level Disparities in the Health of Older Indians: Evidence from Biomarker Data

Economics & Human Biology, Volume 19, December 2015, Pages 145–156

Jinkook Lee, Mark E. McGovern, David E. Bloom, P. Arokiasamy, Arun Risbud, Jennifer O’Brien, Varsha Kale, Peifeng Hu


Using new biomarker data from the 2010 pilot round of the Longitudinal Aging Study in India (LASI), we investigate education, gender, and state-level disparities in health. We find that hemoglobin level, a marker for anemia, is lower for respondents with no schooling (0.7 g/dL less in the adjusted model) compared to those with some formal education and is also lower for females than for males (2.0 g/dL less in the adjusted model). In addition, we find that about one third of respondents in our sample aged 45 or older have high C-reaction protein (CRP) levels (>3 mg/L), an indicator of inflammation and a risk factor for cardiovascular disease. We find no evidence of educational or gender differences in CRP, but there are significant state-level disparities, with Kerala residents exhibiting the lowest CRP levels (a mean of 1.96 mg/L compared to 3.28 mg/L in Rajasthan, the state with the highest CRP). We use the Blinder–Oaxaca decomposition approach to explain group-level differences, and find that state-level disparities in CRP are mainly due to heterogeneity in the association of the observed characteristics of respondents with CRP, rather than differences in the distribution of endowments across the sampled state populations.

Keywords: Biomarkers; Health disparities; Cardiovascular health; Anemia; Aging

Wednesday, September 23, 2015

Future Directions for Well-Being Policy at the Scottish Parliament

Last Friday we held a workshop on the "Future Directions for Well-Being Policy" at the Scottish Parliament. The workshop was funded by the Scottish Institute for Research in Economics (SIRE) and organised by David Bell, Christopher Boyce, and Liam Delaney.

The purpose of the day was to bring prolific national and international speakers to the Scottish debate
Carnegie's influential report on
well-being in Scotland
on well-being based policy. As outlined at the start of the day, Scotland is uniquely positioned to develop a society that focuses on improving well-being. Not only has the Scottish Government developed and successfully embedded into the constitution a National Performance Framework but there is also a critical mass of researchers within academia and policy think tanks eager to take this debate forward. Yet the movement toward a well-being based society is not going to be an easy one and there are key challenges ahead. It was the intention of our workshop to highlight and discuss many of these challenges, as well as build and strengthen links around the well-being community in Scotland and beyond.

The workshop was organised around three key themes:

(1) Measurement: What should we measure? Can we measure it? How do we know we are measuring what we think we are?
(2) Implementation: If we are convinced we are measuring something meaningful, how do we go about implementing measurements and research findings into public policy?
(3) Engagement: Who is involved in the conversation about well-being policy? Who should be involved in the conversation? How can we establish legitimacy? How do we bring people into the conversation?

We invited key speakers who had expertise within each of these areas. We aimed for diversity that would provoke critical and reflective discussion. The introduction to the day, given by co-organiser of the event Christopher Boyce, highlighted the key problem we face: focusing only on economic factors, when we know that for the huge majority living in economically advanced countries, and who have their basic material needs met, higher incomes are not going to add much, if anything, to their well-being, Other factors, such as our social relationships, our health, our workplace environment, and our non-cognitive skills, contribute much more to how we feel about our lives. This talk highlighted that if economic policy were aimed at higher well-being, economic stability would be preferable to higher overall long-term income growth (particularly for those most vulnerable) and reductions in inequality would be a sensible way forward.


We then proceeded to our section on measurement. First, Conal Smith from the OECD spoke on "Measuring Well-Being: Progress to Date and Remaining Challenges". Conal's talk highlighted the ground-breaking work that the OECD has carried out over recent years (to push forward the international well-being agenda), for example though their Better Life Index. Conal, who has been working in this key policy area for a number of years, explained that many of the early concerns with well-being data with regard to reliability and validity have largely been resolved. However, there are clear measurement gaps: things we simply don't yet know and aspects that for the moment cause distrust with the measures. These included issues around (i) understanding cultural bias in answering questions; (ii) how to capture eudaimonic aspects of well-being (meaning and purpose in life: rather than purely hedonic well-being); (iii) how to understand social capital (trust); (iv) how to measure social contact; and (v) designing measures that capture volatility of income. Nevertheless, he presented evidence that cultural bias was not the problem we originally believed it to be - indeed cross-country comparisons may be more reliable than we first thought.

Our next speaker, Liam Delaney from the Behavioural Science Centre here at Stirling University, spoke on "Measurement Issues in Well-Being". As had been hoped, Liam's talk raised more issues than were solved. This is important for serious policy debate and Liam discussed issues with measuring different concepts of well-being (such as reliability, anchoring, interpretation, and reporting heterogeneity). Much complexity arises out of attempting to measure well-being but with new improved technologies (such as biomedical markers) many issues can be solved - however many new issues come to surface. Well-being needs to be assessed multi-dimensionally (a theme that recurred throughout the day - we need more than just one measure to capture well-being) but how do we balance each of these dimensions when it comes to policy?


Just before lunch we heard from our keynote speaker Bryan Smale, over from the University of Waterloo in Canada, to tell us about the Canadian Index of Well-Being (CIW). It is probably fair to say that the the CIW is at the forefront of national well-being policy initiatives, highlighting just the sort of level of measurement and engagement that is needed to have a serious public debate on well-being. We had hoped it would be informative and inspirational for the development of Scotland's own national indicators. The CIW took ten years to move from initial conception to the publication of its first report but this process involved intense public consultation to understand core Canadian values and ensure a sound conceptual and theoretical framework. This has led to 8 domains (not too dissimilar from many other multidimensional scales e.g. OCED Better Life Index) with 8 indicators for each and the CIW report on both an overall composite index, which at the outset, initiated many fruitful conversations about well-being (especially when we compare it with GDP) but ultimately the index is a useful dashboard of indicators which have clear links to policy levers.

The CIW has 8 domains and some have not risen in line with GDP.
GDP has risen much more quickly than the  overall composite index
Interestingly, as we learnt, the CIW is non-partisan, and is more interested in creating real differences on the ground rather than feeding into party political agendas. But it is clearly something that can be, and has been used to develop interventions at all levels of government. And more recently there have been moves to carry out the CIW at the divisional level. However, as with all well-being indices, key challenges exist and these include: data measuring criteria changes across time, geographical differences in measurement, political interference, and learning how to include new topics into the index. Ultimately, though, the CIW is grounded conceptually making many of these issues surmountable in some way. We also heard how various local authorities had used the CIW framework to improve, for example, access to arts and culture and levels of physical activity. Perhaps one of the most fascinating insights was that the CIW did not measure well-being per se but what is known to affect well-being. Of the 8 domains that contributed the most to subjective well-being, it was community vitality, time use, and leisure and culture, rather than health and living standards, that were more important.
CIW domains that contribute to subjective well-being
(size of arrows reflect overall contribution)
Since its launch, the CIW has sparked many conversations about the idea of national well-being in Canada and progress has been both top-down (initial development) and bottom-up (following the initial development it has inspired communities to put pressure on local government). It has helped policymakers think about problems and solutions in the context of the outcomes contained in the CIW. It is clear, as highlighted in our next session, that the well-being agenda needs a more cross-cutting approach that recognises that a policy implemented in education to improve skills and labour outcomes may also have, for example, additional benefits for health, leisure and democratic engagement. Thus, the aim to improve skills may be undervalued if only looked at from a labour market perspective.


In our next session, Wendy Loretto from the University of Edinburgh, and Saamah Abdallah from the New Economics Foundation (NEF), spoke on the theme of implementation. Both gave fascinating talks. Wendy highlighted that there was often a huge gap between practice and policy and yet again another gap between practice and implementation. A growing number of employing organisations introducing have introduced policies to improve and sustain the physical, mental and emotional well-being of their workforce. However, drawing on evidence from two Scottish studies, Wendy showed that there had been many pitfalls in implementing them in practice. These ranged from lack of joined up thinking, lack of follow up, poor design, delays, and non-existent measurement. It was concluded that  (i) poor implementation can reduce, rather than foster, well-being; (ii) context was important; (iii) the same people need to be involved in design, delivery, and implementation; (iv) there needs to be shared responsibility for well-being between employee and employer; and that (v) training and support are needed for managers.

Saamah Abdallah spoke about a range of frameworks and principles promoted by NEF and others. This included their influential "Five Ways to Well-Being" (Connect, Be Active, Take Notice, Keep Learning, and Give), which were designed to help individuals and havealso been used by organisations to develop their own approaches. The use of cost-benefit approaches to well-being, the use of "Well-Being Adjusted Life Years" (WELBY) rather than the better known "Quality of Life Adjusted Life Years" (QALY), and better allocation of resources across governmental departments due to the diverse effects of policies on well-being were all discussed. 

The talk and the questions that followed highlighted the importance of: procedural utility (how you do things matters more than what you do); the fact that we still don't know how to consider future well-being and the danger that sustainability may be crowded out by the well-being debate; and that equipping individuals with capabilities in the Amartya Sen will not guarantee people will thrive, especially if we do not also discuss values.


Our final session of the day was on the theme of engagement. An area that needs much focus - particularly here in Scotland where there is little public knowledge of, for example, Scotland Performs and the National Performance Framework. If we want the well-being debate to achieve legitimacy, then engagement is key. But perhaps more importantly empowering individuals through debate has the potential to have wide benefits to national well-being. Christina Victor from Brunel University leads 'culture and sport' strand of the What Works Centre for Wellbeing programme. They have already carried out key stakeholder engagement as part of their program and it was explained that this will shape conversations about the dimensions of wellbeing to be addressed in their evidence synthesis and the key aspects of social diversity and contextual factors that combine to determine what works, for whom, and in what circumstances.

Karen Scott of the University of Newcastle and author of the book "Measuring wellbeing: towards sustainability", who recently co-organised an ESRC seminar series on the politics of well-being, gave our final talk. Karen also has experience in working with local authorities to try and help them incorporate well-being based initiatives. In the talk it was suggested that well-being was not, in fact, a science but fundamentally a political process which science may help us understand. Her talk highlighted the dialogue around well-being as to who can speak about well-being and who is marginalised. There was a concern with why the debate was dominated by psychologists and economists and there were wider issues around which other disciplines, including politics, needed to have a voice. One overarching theme that came out in this talk was that serious well-being discussion brings up uncomfortable topics that many perhaps did not want to discuss. Many local communities are affected by economic systems they cannot control and have no voice or capability with which to tackle such issues. Also, it was suggested that some well-being measures were not entirely relevant - for example, why are we so concerned with how many people eat 5 or more fruits and vegetables rather than how much McDonalds spends on advertising? Karen highlighted that the well-being agenda has. in many respects, been co-opted by organisations to support certain agendas. For example, NEF's Five Ways to Well-Being approach can be contentious and whilst helpful for dealing with difficult situations, such as disability or loss of a loved one, can also be used negatively to create a dis-empowering blame culture, whereby individuals are ultimately to blame rather than the society which has shaped their goals and aspirations. For example, subjective well-being might be used to blame vulnerable individuals for their situations. Her concern was that those interested in the well-being agenda did not always want to talk about the problems with the well-being agenda. There are no ultimate truths in well-being research as someone is always likely to be marginalised and we need an ongoing critical debate, to which science must contribute.

The last talk was an excellent one to end on and summed up much of the day and the discussion we had following each of the talks. There are important issues ahead, and by shying away from confronting these, we will do the well-being policy agenda a huge disservice. The political dialogue around well-being is not going to disappear and perhaps one day, if we overcome many of the challenges, we might find ourselves living in a society where well-being takes centre stage. In the meantime, and it may be a long time, well-being research has a useful role in informing us all how we can thrive at least a little in our daily lives. 

Sunday, September 20, 2015

Putting your money where you can’t touch it

In a recent conversation with a financial advisor, he told us that some of his customers prefer not to have the ability to withdraw money from their saving accounts at any time. Instead, they prefer having to contact the financial advisor in order to gain access to their money. The natural question to ask is: Why do some people want to "bind their hands" and intentionally make it more difficult to withdraw and spend their money?  
Credit Card in ice : Stock-Foto
Credit card in ice (Getty images)
Behavioural economic theory provides an answer: Individuals who predict that they will be tempted in the future to withdraw money and use it for unwise purposes will want to put their money where they can’t touch it. They have a demand for commitment. In this post, we will summarise a few recent insights from the economics and the psychology of commitment in the context of financial decision making.
A key insight in the economic literature on commitments is that there is individual heterogeneity; not everybody wants to reduce their access to their saving accounts. Some individuals are just not tempted by the prospect of withdrawing money and spending it impulsively. They have no demand for commitment simply because they do not need it as their preferences do not change over time and are dynamically consistent. Other individuals tend to be tempted by the prospects of early cash withdrawals as they are present-biased. If present-biased individuals, however, do not possess enough self-awareness to predict future temptation episodes and thus are naïve, they do not demand commitment strategies because they overestimate their future financial discipline. The group who will have a demand for commitment is composed of individuals who are present-biased and tend to be tempted by cash withdrawals, and anticipate these temptations. Behavioural economics calls them sophisticated.
Demand for commitment accounts and present bias
Without present bias
With present bias
No demand for commitment because of time-consistent preferences
No demand for commitment due to lack of awareness of potential self-control problems
Positive demand for commitment because of awareness of potential self-control problems

Hence, individual differences in saving decisions are not only determined by cognitive biases, but also by the degree to which individuals are aware of these biases. For example, Goda et al. (2015) suggest that self-awareness regarding one's biases can be a stronger determinant of financial behaviour than the biases themselves. They find that self-awareness of potential biases has a positive effect on retirement savings even after controlling for measures of IQ, financial literacy and socio-demographic characteristics.
In an experimental setting, Beshears et al. (2015) investigate how many individuals are present biased and sophisticated by testing whether individuals have a demand for illiquid commitment savings accounts. Individuals could choose how to allocate money to an illiquid commitment account or a normal savings account without penalty for withdrawal. The interest rates of both accounts were varied. Participants allocated around half of their endowments to the commitment account when there was no difference in interest rates between the two vehicles, and one-quarter of their money even when the interest rate paid by the commitment account was lower than the liquid account. These findings suggest the presence of sophisticated present-biased individuals in the U.S. adult population. Beshears et al. (2015), however, also find evidence that the U.S. adult population contains naïve present biased individuals and/or individual who have consistent time-preferences.
Further support for the existence of sophisticated present-biased individuals comes from psychology. As outlined here, psychologists are in the process of re-defining the nature of trait self-control. The conventional view that high trait self-control is related to a strong ability to resist temptations is weakened in favour of the view that individuals with high scores on the trait self-control scale avoid being exposed to the temptations in the first place. This proactive use of self-control is only possible if individuals are aware of their self-control problems, i.e. are sophisticated.
Policy and business implications
How can policy-makers and business people such as financial advisors make use of these insights? Most importantly, the insights suggest that a one-size-fits-all policy regarding penalties for early withdrawals is likely to be problematic. As Beshears et al. (2015) point out, higher penalties for early withdrawal may either discourage or encourage savings, depending on whether individuals are present-biased and whether they know about it. Setting high withdraw tax penalties might increase savings of sophisticated individuals, but might reduce savings of naïve and dynamically consistent individuals as they do not like to put their money where they cannot touch it.
Instead, financial advisors could explicitly consider individual heterogeneity when advising their customers about the best ways to save. Eliciting whether individuals tend to engage in impulsive purchases from time to time would allow financial advisors to suggest specific saving vehicles with and without penalties for early withdrawal. To elicit whether individuals are present-biased, naïve, or sophisticated, short questionnaires could be offered to the customers to improve the advice.

References and further reading:
Angeletos, G; Laibson, D; Repetto, D; Tobacman, J; Weinberg, S. (2001). The Hyperbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation. Journal of Economic Perspectives, 15 (3), p. 47–68.
Beshears, J; Choi, J; Harris, C; Laibson, D; Madrian, B; Sakong, J. (2015). Self Control and Commitment: Can Decreasing the Liquidity of Savings Account Increase Deposits. NBER Working Paper Series, No 21474, August. Available at: .
Delaney, L; Lades, L. (2015). Present Bias and Everyday Self Control Failures. Stirling Economics Discussion Paper, 2015-01, University of Stirling, July. Available at: .
Ent, M. R; Baumeister, R. F; and Tice, D. M. (2015). Trait Self-control and the Avoidance of Temptation. Personality and Individual Differences, 74, p. 12–15.
Goda, G; Levy, M; Manchester, C; Sojourner, A; Tasoff, J. (2015). The Role of Time Preferences and Exponential-Growth Bias in Retirement Savings. NBER Working Paper Series, No 21482, August. Available at: .
Laibson, D. (1997). Golden Eggs and Hyperbolic Discounting. Quarterly Journal of Economics, , 62 (2), p. 443–477.
O’Donoghue, Ted; Rabin, M. (1999). Doing It Now or Later. American Economic Review, 89 (1), p. 103–124.
O’Donoghue, Ted; Rabin, M. (2001). Choice and Procrastination. Quarterly Journal of Economics, 116 (1), p. 121–160.
O’Donoghue, Ted; Rabin, M. (2015). Present Bias: Lessons Learned and to be Learned. American Economic Review: Papers & Proceedings, 105(5), p. 273–279. Available at: .

Blog post by Bernardo Nunes and Leonhard Lades

Friday, September 18, 2015

How can social psychology contribute to policy? [SPSP Convention Jan 28-30, 2016]

Hot on the heels of Obama's executive order on the use of behavioral science in the US government comes this announcement by the Society for Personality and Social Psychology. Next year's 17th annual SPSP convention will take place in San Diego on January 28-30 and will feature a session called "How Can Social Psychology Contribute to Policy?". Details below:

"SPSP's 2016 President Wendy Wood will be hosting a discussion that will answer questions ranging from, "are policy jobs out there for me?" to, "how can we improve the world by developing science-based policy?" We will all be asking such questions, given President Obama's 2015 Executive Order to use behavioral science insights to better serve the American people.

Collaborating with Government: One Example and Many Proposals
Michael I. Norton, Professor, Harvard Business School
Norton will present an experiment with the city of Boston, Massachusetts where increasing operational transparency—showing the work being done for citizens – improved perceptions of government. He will then review his co-editorship of an issue of Perspectives on Psychological Science: Memos to the President from a “Council of Psychological Advisors.”

Craig FoxBridging the divide between social science and policy
Craig Fox, Professor, University of California Los Angeles
Policymakers are increasingly receptive to insights from social science, yet these scientists rarely have direct impact on policy with their research.  In my talk I’ll derive lessons from the success of neoclassical economists and enterprising behavioral scientists in influencing policy, and motivate a more effective approach to behavioral policy research.

Richard ThalerMisbehaving: The Making of Behavioral Economics
Richard Thaler, Professor, University of Chicago
How behavioral economics recognizes human miscalculations and their effects on markets and policy. Understanding how people actually behave can inform policy and lead to better decisions in our lives, our businesses, and our governments"

Wednesday, September 16, 2015

Barack Obama issues executive order regarding the use of behavioral science in government

Extraordinary news for anyone interested in behavioral science. President Obama yesterday issued an executive order saying "A growing body of evidence demonstrates that behavioral science insights ... can be used to design government policies to better serve the American people". It recommends that "the Federal Government should design its policies and programs to reflect our best understanding of how people engage with, participate in, use, and respond to those policies and programs." 
The order is accompanied by the release of the first annual report of the recently formed White House Social and Behavioral Science Team. The report describes the team's interventions in areas such as retirement savings, college enrollment, health insurance, microloans and government efficiency. It is similar to the recent UK Behavioural Insights Team 2013-15 Update Report which is also worth reading for policy nerds.
Liam has written on the blog before about the growing influence of behavioural science (behavioral economics + psychology) on public policy. This importance of this shift should be viewed in the broader context of the fact that neoclassical economics has historically been the only discipline which has really affected policy-making. My view is that Thaler and Sunstein's book Nudge has been very influential in steering this trend. Nudge was released in mid-2008, a time when a lot of people regarded mainstream economics as discredited by its complete failure to anticipate the Great Recession. The book summarized decades of work in behavioral economics and psychology and suggested practical ways in which this research could inform policy, and its ideas were more-or-less immediately embraced by the UK Conservative party. Two years later, Conservative prime minister David Cameron created The Behavioural Insights Team to implement the book's ideas within government. The BIT has since published over a dozen reports describing how it has applied ideas from behavioral science to a diverse number of policy areas, and the book's ideas have influenced organizations like the House of Lords, the United Nations, the World Bank, the OECD, the FCA and many others. 
Obama's executive order should be seen as very positive development by anyone who wants to see more realistic models of human decision-making used in policy-making.
Full executive order quoted below. September 15th, 2015, remember the date!

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A growing body of evidence demonstrates that behavioral science insights -- research findings from fields such as behavioral economics and psychology about how people make decisions and act on them -- can be used to design government policies to better serve the American people.
Where Federal policies have been designed to reflect behavioral science insights, they have substantially improved outcomes for the individuals, families, communities, and businesses those policies serve. For example, automatic enrollment and automatic escalation in retirement savings plans have made it easier to save for the future, and have helped Americans accumulate billions of dollars in additional retirement savings. Similarly, streamlining the application process for Federal financial aid has made college more financially accessible for millions of students.
To more fully realize the benefits of behavioral insights and deliver better results at a lower cost for the American people, the Federal Government should design its policies and programs to reflect our best understanding of how people engage with, participate in, use, and respond to those policies and programs. By improving the effectiveness and efficiency of Government, behavioral science insights can support a range of national priorities, including helping workers to find better jobs; enabling Americans to lead longer, healthier lives; improving access to educational opportunities and support for success in school; and accelerating the transition to a low-carbon economy.
NOW, THEREFORE, by the authority vested in me as President by the Constitution and the laws of the United States, I hereby direct the following:
Section 1. Behavioral Science Insights Policy Directive.
(a) Executive departments and agencies (agencies) are encouraged to:
(i) identify policies, programs, and operations where applying behavioral science insights may yield substantial improvements in public welfare, program outcomes, and program cost effectiveness;
(ii) develop strategies for applying behavioral science insights to programs and, where possible, rigorously test and evaluate the impact of these insights;
(iii) recruit behavioral science experts to join the Federal Government as necessary to achieve the goals of this directive; and
(iv) strengthen agency relationships with the research community to better use empirical findings from the behavioral sciences.
(b) In implementing the policy directives in section (a), agencies shall:
(i) identify opportunities to help qualifying individuals, families, communities, and businesses access public programs and benefits by, as appropriate, streamlining processes that may otherwise limit or delay participation -- for example, removing administrative hurdles, shortening wait times, and simplifying forms;
(ii) improve how information is presented to consumers, borrowers, program beneficiaries, and other individuals, whether as directly conveyed by the agency, or in setting standards for the presentation of information, by considering how the content, format, timing, and medium by which information is conveyed affects comprehension and action by individuals, as appropriate;
(iii) identify programs that offer choices and carefully consider how the presentation and structure of those choices, including the order, number, and arrangement of options, can most effectively promote public welfare, as appropriate, giving particular consideration to the selection and setting of default options; and
(iv) review elements of their policies and programs that are designed to encourage or make it easier for Americans to take specific actions, such as saving for retirement or completing education programs. In doing so, agencies shall consider how the timing, frequency, presentation, and labeling of benefits, taxes, subsidies, and other incentives can more effectively and efficiently promote those actions, as appropriate. Particular attention should be paid to opportunities to use nonfinancial incentives.
(c) For policies with a regulatory component, agencies are encouraged to combine this behavioral science insights policy directive with their ongoing review of existing significant regulations to identify and reduce regulatory burdens, as appropriate and consistent with Executive Order 13563 of January 18, 2011 (Improving Regulation and Regulatory Review), and Executive Order 13610 of May 10, 2012 (Identifying and Reducing Regulatory Burdens).
Sec. 2. Implementation of the Behavioral Science Insights Policy Directive. (a) The Social and Behavioral Sciences Team (SBST), under the National Science and Technology Council (NSTC) and chaired by the Assistant to the President for Science and Technology, shall provide agencies with advice and policy guidance to help them execute the policy objectives outlined in section 1 of this order, as appropriate.
(b) The NSTC shall release a yearly report summarizing agency implementation of section 1 of this order each year until 2019. Member agencies of the SBST are expected to contribute to this report.
(c) To help execute the policy directive set forth in section 1 of this order, the Chair of the SBST shall, within 45 days of the date of this order and thereafter as necessary, issue guidance to assist agencies in implementing this order.
Sec. 3. General Provisions. (a) Nothing in this order shall be construed to impair or otherwise affect:
(i) the authority granted by law to a department or agency, or the head thereof; or
(ii) the functions of the Director of the Office of Management and Budget relating to budgetary, administrative, or legislative proposals.
(b) This order shall be implemented consistent with applicable law and subject to the availability of appropriations.
(c) Independent agencies are strongly encouraged to comply with the requirements of this order.
(d) This order is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity by any party against the United States, its departments, agencies, or entities, its officers, employees, or agents, or any other person.

Monday, September 14, 2015

Second Dublin BE Meet-up

New: Mailing List and Website for Irish Behavioural Science, Economics and Policy Network

Since 2008 a number of us have organised an annual conference for people working at the interface of economics, psychology and related areas. Speakers have included international thought-leaders in this area including David Laibson, David Halpern, Robert Sugden, Arie Kapteyn, Ruth Byrne and John O'Doherty as well a diverse range of speakers from across economics, psychology and policy in Ireland and they have contributed to maintaining an active discussion of the potential for this area in Ireland. The next one will take place at the ESRI in Dublin on November 27th. At the previous session we agreed to organise some more adhoc meet-ups in between the events partly to disseminate new ideas and also with a view to establishing a more structured network in this area in Ireland.

The first of these meetings took place in Dublin on July 22nd organised by myself and Sean Gill.  There were 4 presentations from myself, Pete Lunn, Michael Daly and Sean Gill and discussion about future events and the role of behavioural economics more generally. Meet-ups around this area are now taking place in several cities including London and Sydney. There are many people interested in this broad area in Dublin and Ireland more generally as was reflected in the healthy attendance from academia, policy and business. This is intended to a broad forum and we welcome attendance and contribution from academics interested in exchanging ideas with a broad audience, people across different areas including students and people with business and policy interests in this area. For now we envisage the events being structured around short talks where a speaker describes briefly an idea they are working on or thinking about and potentially some suggestions for collaboration. Though there are many other event formats that could be considered.

The next one will take place on September 30th at 6.30pm. Our three speakers are Kenneth McKenzie from Target McConnells, Anne-Marie Farrell from Google and Gerard O'Neill from Amarach Research.