Emotions in economic interactions

How do you get people to cooperate. By increasing utility, of course, but that is difficult to measure, obviously, and there may some components beyond rationality in emotional contexts. However, we have some interesting ways to get some neurological hints about positive and negative emotions by measuring the conductance of skin. This may help to explain why people are sometimes willing to hurt themselves in order to punish others.

Mateus Joffily, David Masclet, Charles Noussair and Marie-Claire Villeval conduct an experiment where cooperation, free-riding and punishment can happen. They measure skin conductance to reveal the intensity of emotions and let players reveal whether their emotions are positive or negative. Cooperation and punishment of free-riding elicit positive emotions, the latter indicating that emotions can override self-interest. That is also because punishment relieves some of the negative emotions from observing free-riding. And one does not like being punished, which lends one to cooperate more in the future. Finally, people like being in a set-up where sanctions are possible, in particular because it allows a virtuous circle of emotions that reinforce each other and lead to more cooperation.

Information with negative value

We all have regretted some decisions we have made. But different individuals respond differently to this. Some would say "oh well, I would have done differently had I known, but this is best I could do at the time." Others are more like "OMG this is horrible, you should not have told me." An individual of the second kind finds negative value in any ex-post information and thus wants to live in a world with a different information structure from the first.

Emmanuelle Gabillon formalizes this idea and studies structure where information is available before ("flexible") or only after ("non-flexible") decisions are taken. The paper also derives the characteristics a regret utility function should have (in particular, it cannot have "rejoicing"). Information can only have negative value in the non-flexible case if preferences exhibit concavity with respect to the ex post best outcome. Interestingly, information can also have negative value in the flexible case for a regretful person. Indeed, while information is useful for all people in revising the expected utility of strategies, for a regretful person it also is useful to revise expected regret. One can thus become even more conservative and this can lead to outcomes that are inferior in expectation to those where one would not have had the information.

As expected, lottery players are not rational

There is no mystery that under normal circumstances, homo oeconomicus does not play the lottery. Exceptions arise when there is enjoyment in playing the lottery (is this why slot machines a so popular in the US?) or when there are particular reasons. But casual observation indicates people play the lottery, and a lot. Maybe these circumstances mentioned above are met, maybe they are not rational economic agents.

Claus Bjørn Jørgensen, Sigrid Suetens and Jean-Robert Tyran would say lottery players, at least some of them, have a peculiar sense of probabilities. While many change their numbers, among those who change, many avoid numbers that have recently been drawn, as if the lottery were a drawing without replacement. But if a number is on a streak (drawn a few times in a row), then they choose it. If margins were not so high in lotteries, one could possibly make money by arbitraging against these people trying to predict the lottery numbers. But you can actually getting a positive return from lotteries by only buying tickets when large jackpots are at stake. The International Lottery Fund based in Australia is there to prove it.

On the perception of time

As you age, time flies faster. The same applies to when you are busy. Psychologists have long studied how the perception of time varies with circumstances, but economists have barely touched the subject. If the perception of time depends on one's age, the fix is easy: adapt the discount factor to the life cycle (beyond what the probability of death implies, that is a separate matter), and you are ready to study the savings behavior across generations, etc. But this can be more subtle than that.

David Aadland and Sherrill Shaffer point out that the implied discount factor may not necessarily be exogenous. If you choose to have a busy life, you also modify your discount factor. If you are rational, you must take this into account in your occupational choice. I have certainly noticed how time has been flying much faster in the past years, and writing this blog beyond my normal work duties has certainly contributed to being busier than usual. I have asked myself whether the time spent on this is worth it, and now that I realize I may also reduce my enjoyment of time, I need to question it even more. But beyond these egocentric ramblings of mine, Aadland and Shaffer show that these consideration could explain why people want to scale back as they age the time they spend working, or why they retire earlier that previous generations despite expecting to live longer. They also find that unless people anticipate the changes in time perception, optimal plans are not time-consistent.

Who is rational?

Much of the experimental economics literature is about finding situations where some of the fundamental axioms of rational utility theory are violated. And you are always going to find someone who does not act rationally. But this literature often understates how often people are actually rational and how this translates into better outcomes.

Syngjoo Choi, Shachar Kariv, Wieland Müller and Dan Silverman study the characteristics of rational people. Specifically, they conducted a field experiment on 1182 households in the Netherlands to find whether they behaved consistently with revealed preference theory. They then combine these results with a large array of socio-demographic and economic characteristics. They find that the more rational people are, the higher income and education they have. Nobody will be surprised to learn that men are more consistent, but I am shocked to see that young people are more rational. Why would life experience make you deviate from rationality? Finally, the impact of rationality on outcomes is substantial: a one standard deviation increase in consistency is associated with a 15-19% increase in wealth.

Modelling without theory

In Economics, we have adopted the scientific method much like other sciences. As we teach our students, it consists of the following steps

1. Observe regularities in the data.
2. Formulate a theory.
3. Generate predictions from the theory (hypotheses).
4. Test your theory (is it consistent with data?)

In the context of Economics, the goal of the procedure is not only to explain why regularities in the data happened, but also to build a theory that is useful in predicting the consequences of particular policies or institutional designs.

David Hendry just published a paper about the scientific method in Economics that appears to fly in the face of what I just described. Here is an attempt to summarize his stand, and I apologize for quoting quite liberally:

1. Specify the object for modeling, usually based on a prior theoretical analysis in Economics. An example of such an object is y=f(z).
2. Defining the target for modeling by the choice of the variables to analyze, y and z, again usually based on prior theory. This is about deriving the data-generating process of the variables of interest, or fitting an equation with some statistical procedure.
3. Embed that target in a general unrestricted model (GUM), to attenuate the unrealistic assumptions that the initial theory is correct and complete. The idea is to add other variables, lags, dummies, shift variables and functional forms to improve the empirical accuracy of the initial model.
4. Search for the simplest acceptable representation of the information in that GUM. Or, now that the model has become huge (and may contain more variables than data points), let us get rid of some of them without loosing too much in accuracy.
5. Rigorously evaluate the final selection: (a) by going outside the initial GUM in step three, using standard mis-specification tests for the ‘goodness’ of its specification; (b) applying tests not used during the selection process; and (c) by testing the underlying theory in terms of which of its features remained significant after selection.

In other words, this amounts to take some linearized version of some theory, through data at it, whether theoretically relevant or not, then massage it until it fits the data.

A part from the fact that this is really the blueprint for an automated data mining exercise that is not driven in any way to answering a particular policy question, this procedure not only disregards the scientific method, but also Occam's Razor and the Lucas Critique. What use is it to learn that the CPI follows a polynomial of degree five with three lags on exports of cabbage, the number of sunny days, 25 other variables and three structural breaks (not an actual example used by Hendry, but it could)? If you want to make some very short term forecasts, that may be accurate, and this method is abundantly used in the City or Wall Street by neural networks "experts." But when it comes to advising policymakers, you need to have some Economics, and by that I mean economic theory, to explain why economic agents behave in such a way and what an intervention would lead to.

The scientific method starts with the observation of the data. Hendry dismisses this with a slight of hand, stating that stylized facts are "an oxymoron in the non-constant world of economic data." What if there are constants in economic data? In fact there are plenty, and this is what theories are trying to explain. Has Hendry never observed something in his surrounding that he then tried to explain? Or does he really spend his days feeding linear equations into his computer to see what it can come up with with his database?

Such papers, especially by people who enjoy respect like Hendry does in the UK, deeply upset me. To top it off, there are 33 self-citations.

The spaceship problem

Suppose you have to plan a very long term mission in space. It will last for many years, and you need to provide a group of people the means to live in a hermetic environment. You do not have access to Star Trek technologies like warp speed, replication and teleportation. Your population can reproduce, but life length and quality of life depends on resources and population density. How many people should be on such a mission? This is known as the spaceship problem. Of course, economists have something to say about this.

Pierre-André Jouvet and Grégory Ponthière are not going to solve the problem, there are too many biological and physical constraints, but they point out that the solution will yield solutions that contradict utilitarianism. They focus on the trade-off between the number of people and their life length. Indeed, longevity impacts population size and thus density. They assume that a social planner uses the sum of residents' utilities as a criterion and, unfortunately, that resources are unlimited, which makes the paper stray away from Economics.

What Jouvet and Ponthière really want to do it is compare different social welfare criteria in this environment. The Classical Utilitarian, for example, sums the utility of all individuals, the Average Utilitarian only the living ones. In a model without reproduction and a finite mission time, Classical Utilitarianism yields a small population living very long, while the second may want to have a large population that lives for a short time. Add reproduction to the mix and anything can happen depending on parameters values and initial population size. Make the mission life infinite, and the authors run into problems and need to define additional social welfare parameters. That is mainly due to the fact that there is no discounting, and infinitively lived economies and ill-defined.

What do I learn from this exercise? It is not very clear, except that social welfare criteria matter, adding utilities gives us a lot of trouble and that discounting is essential. But we knew that already, even when the spaceship is called Earth.

Properly weighting social welfare functions

When it comes to evaluating optimal policies, one has to define a social welfare criterion. That is problematic as soon as there is heterogeneity. A popular criterion is Pareto Optimality, but this is a weak criterion in the sense that it is not very restricting. Or one can look at a political equilibrium, but this ignores how much people care about various policy outcomes. Another way that makes microeconomic theoreticians cringe is to add up the utility of everyone. They cringe because utility functions are only defined up to a Paretian transformation, and thus not comparable across individuals. But sometimes you have to find a way, and it is commonly assumed that all individuals have the same utility function, but potentially different utilities. Yet, adding utilities up has the drawback to the optimal policy will always be about equalizing income and consumption across individuals, because poorer ones have a higher marginal utility of consumption. But not all policies should be primarily about redistribution. The typical solution to this problem is to apply so-called Negishi weights, which essentially freezes the initial distribution of income, and thus allows to concentrate on the purpose on the policy.

Alexis Anagnostopoulos, Eva Carceles-Poveda and Yair Tauman offer a different solution to this problem. While Negishi amounts to weigh each individual by the inverse of her marginal utility at the maximal outcome, this results relies on the existence of complete markets. Under incomplete markets, the set of weights may be different. To give credit to the precise formulation of the problem, I quote the authors here:

We first define for every set of individual weights and for every social welfare function the contribution of every individual to the total welfare through the individual’s initial endowments. We then provide an axiomatic approach to the notion of the per unit contribution of every good and every individual, where the contribution of an individual to the total welfare is the total contribution of his initial endowments. We then define a set of individual weights to be proper iff the weighted utilities of every individual from this allocation are proportional to the contribution of the individual to the total welfare as defined by this set of weights.

The axiomatic approach consists of four axioms that characterize an elegant family of contribution mechanisms. The first axiom asserts that the per unit contribution should be independent of the units of measurement of the goods. The second asserts that if two (or more) goods play the same role in the welfare function, they should have the same per unit contribution. The third axiom asserts that if the welfare function can be broken into different components, then the per unit contribution of a given good is the sum of the per unit contributions arising from the different components. The last axiom guarantees that the per unit contribution is a continuous mapping with respect to an appropriate norm.

It is shown that every contribution mechanism that satisfies these four axioms is uniquely determined by a non negative measure on the unit interval. The selection of a specific contribution mechanism (or equivalently the selection of a specific nonnegative measure on the unit interval) determines for a given economy and a given set of weights a proper constrained efficient allocation and a proper set of weights.

This is a very exciting paper that should lay the foundation for a better assessment of policies than the silly adding up of utilities that is typically done.

Behaviorial economics is futile so far

Neoclassical economics has taken a lot of flak recently, I think unjustly, for failing to predict the last economic crisis. For many critics, behavioral economics is the next big idea, because it is much more closely tied to empirics and has a special focus on irrational behavior. But beware of fads, of which there are unfortunately too many in Economics.

Nathan Berg and Gerg Gigerenzer say that behavioral economics is just as bad as neoclassical economics because they are both full of ad hoc assumption and build on axioms that are not tested. In particular, the deviations from rationality are never evaluated in how costly they are, for example whether people are then poorer or less happy. This is important as if those deviations are costly, people would likely do something about them and they may become less important. In other words, behavioral economics if far from being mature enough to be the panacea some are seeing in it.

On the emergence of money

Why are we using money? The answer we give to undergraduates is that money facilitates transactions and can be use as a store of value. But how do we get there? For money to be used, especially fiat money, there needs to be an agreement among many people that a particular commodity is the right one, and that we should all accept it for payment. How do you get there? If you look at the economic history of humanity, the use of money is in fact only a very recent phenomenon, and many previous attempts at introducing money failed. What makes money stick? All these are questions that are really difficult to answer and that will keep scholars busy for a long time. What we have so far are partial answers that are mostly of anecdotal nature.

Xue Hu, Yu-Jung Whang and Qiaoxi Zhang use a trading post approach to understand the emergence of money. A trading post economy includes households with heterogeneous endowments and wants who go to particular locations to meet and trade, and each trading post deals with only two goods. Under a monetary equilibrium, all trading posts deal with the same good, and another one that is different for each. The question is how to get there. The classic paper here is by Peter Howitt and Robert Clower, which was criticized for not having any maximization: this happened by pure chance, but eventually almost all experiments resulted in monetary equilibria. Hu, Whang and Zhang add to this utility maximizing households, but add substantial frictions to prevent convergence from happening too fast. These assume that there is a tâtonnement process that allows only 20% of households how want to switch trading posts to do so.

The conclusions are similar to Howitt and Clower, though. The good most likely to become money is the one that is the most saleable, either because there are large endowments and want for it, or because its trade is less costly. They also find that the absence of double coincidence of wants, traditionally used to justify the existence of money, actually makes the emergence of money more difficult. When money has not yet emerged, why would you experiment in trading your good for something you do not want?