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 losing 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?

Trying to justify IS-LM

The IS-LM model is still not dead. Created to reflect the interaction of aggregate markets, it suffered from the rise of dynamics and microfoundations in macroeconomics, yet remained the staple of undergraduate macroeconomics because a generation of teachers knowing nothing else needs first to die out. Yet, even people in research have clung to it, trying to find the microfoundations of IS-LM, which seems to me completely backward. The scientific method should indicate that you build a theory from observations, then create its graphical representation (if possible), and not trying to justify a graphical representation with some theory.

But anyway, I was thinking about these vain efforts while reading a paper by Ingrid Größl and Ulrich Fritsche, whose goal is to show that the standard Neo-Keynesian DSGE model cannot be represented appropriately in the IS-LM framework. So what? Life is more complex than IS-LM, so deal with it and drop IS-LM. But anyway (again), let us see what their arguments is.

First, the claim is that a Taylor Rule is a poor substitute for the LM curve, because it neglects the store of value role of money. And the DSGE model cannot capture the IS curve because is assumes that savings always equal investment, and people never save in unproductive money. The final claim is that an overlapping generation model is better for the IS curve. Now let us see how these claims are formally made. The model starts with a standard Neo-Keynesian representative agent, who has intertemporal preferences over consumption, leisure and real money holdings. Real Money holdings? Why not question that while you are arguing about the role of money in a model? Why would I care about how much money I carry? Not because I need it for transactions, because current consumption is already there. Not because of the wealth it represents, because future consumption is also there. It is simply there because otherwise the LM curve would not exist. How wrong is that?

Then what about the firm? It produces goods proportionally to the number of employees. Where is capital? Are we now trying to derive an IS curve (where I stands for investment) without investment? Not very convincing. Does the resulting model have anything to do with observed facts? Nothing is offered by Größl and Fritsche. What do I take from this paper? To justify LM, one needs to force people to demand money just because, and to justify IS, one needs to assume away investment. Great.

Money demand: financial adjustment cost, not cash-in-advance

I find monetary models very frustrating. While there is empirical evidence that money is not completely neutral over the range of a couple of years, theory has so far not come up with a believable way to understand why this would happen. The models that come closest have completely outrageous assumptions, such as the infamous Calvo pricing hypothesis I was venting about just a few days ago. This is usually accompanied by money-in-the-utility-function (sure, we all love to walk around with a lot of cash) or with the cash-in-advance constraint. Let us consider the latter a bit more closely.

Essentially, this constraint assume that households have to carry cash for some purchases. Often these models are calibrated to quarterly frequency, because this is what the data bears. This implication is that people have to carry cash for all their purchases in the next three months! How reasonable is that? Or the constraint is imposed on firms for their investment or wage payments, which about as outrageous. Yet, cash-in-advance is used all over, either blindly or because it easily generates a money demand. Of course, as people are forced to demand money.

What monetary urgently needs is a better theory of money demand. People hold money in small amounts because it facilitates transactions. They also hold some as a store of value, as any principles of economics student can recite. But this is not a good solution, as money is dominated in return by almost any asset. People hold money due to some frictions on financial markets, and these holdings are temporary. Now having both these features makes it difficult for the model builder. Which one is more relevant?

Xavier Ragot tells us financial frictions are. For one, looking at data, the distribution of money across households looks much more like the distribution of financial assets than that of consumption. He tries to match both distributions using a model with cash-in-advance for consumption (slightly modified to account for the fact that the rich can buy more on credit), a fixed cost for financial transactions, borrowing constraints and idiosyncratic shocks to household productivity. The model has two degrees of freedom to match the distributions of money, consumption and financial assets: the fixed cost for adjusting your portfolio and the transaction technology parameter from the cash-in-advance constraint. Two values are then obtained, and by turning each of them to zero, Ragot concluded that 85% of money demand comes from financial frictions, and 15% from cash-in-advance transactions. Conclusion: if you want a simple model of money demand, do not rely on cash-in-advance.

About envy

Homo œconomicus is greedy, but why? One explanation is that he is envious, and this makes him competitive, leading to the positive outcomes of market economies that we often tout.

Not so fast, says Boris Gershman. Envy can lead to alternative equilibria, some virtuous, some vicious. We are familiar with the virtuous ones, where people "keep up with the Joneses", and thus exert effort towards getting better. Too much effort in fact. But things can also go dramatically the other way, where the best endowed people restrain their efforts to prevent the destructive envy of the poor. This suboptimal effort is likely to occur when there is large inequality and poor property rights.

This last point is important. For example, Russia right after the fall of communism came almost to a standstill because many were envious of successful people in a very negative way. The successful ones later overcame this by hiring their own security forces, a very inefficient use of resources. And this gave the opportunity to organized crime to take hold. Another example can be found in many developing economies where successful members of a family are expected to contribute significantly to the family. This lowers incentives for effort considerably.

There is now a lot of talk of how the increasing inequality in incomes and wealth could have negative consequences. While this paper shows that inequality will get reduced, it comes at the cost of having the most efficient in the society providing less effort than they would be willing to provide otherwise. Gershman suggests this can be avoided by protecting this elite more.

So, how large is the equity premium?

The equity premium puzzle is probably one of the most controversial puzzle in economics. For one, it is rather difficult to measure properly the equity premium, second the puzzle is about a risk aversion parameter that is itself difficult to measure, and third the literature is pretty much all over the place.

Casper van Ewijk, Henri L.F. de Groot and Coos Santing perform a meta-analysis on the topic: they gathered all the papers about the equity premium they could, could the premises and the results and tried to make some sense from all this. They conclude that the equity premium tends to disappear with time and development, and with lower GDP volatility. Thus is should be normal that the equity premium kind of vanished during the Great Moderation. Now get a theory to replicate this. Assume that financial markets develop as time goes, and you can easily obtain a reduction in the equity premium in any sensible model. No need for strange preferences, complex arguments about taxes, or large improbable events.

That said, the equity premium puzzle is about the size of the premium, not how it changes. But given the fact that it seems to be shrinking, the puzzle may soon be moot.

Household size heterogeneity and the representative agent

A large, but shrinking, proportion of models in macroeconomics assume the existence of a representative agent. While there is clear evidence that households are heterogeneous, under some conditions aggregation may still hold. But even if it does not, what matters is whether it makes a quantitative difference.

Christos Koulovatianos, Carsten Schröder and Ulrich Schmidt address the question from a different angle. They ask whether differences in household size matter. They show it does not, theoretically, if the utility functions exhibit household-size economies (beyond subsistence consumption) that are invariant with income. I think that what the authors want to say here is that household size does not matter as long as preferences are such that consumption demand is linear in household size, which in fact does not imply that preferences are heterogeneous. Household size is just an argument in the household utility function. Or: household utility can be aggregated for individual utilities if the decisions rules can be aggregated, which is the case when they are linear.

Anyway, beyond these semantic issues, Koulovatianos, Schröder and Schmidt then provide survey evidence documenting that preferences have indeed the required property, namely that households perceived that the income necessary to maintain a given standard of living is linear in the number of its members. What is interesting here is that the survey covers many countries, possibly preempting the criticism I mentioned the other day.

On a separate note, this paper is particularly painful to read. It runs for 89 pages, and it is very confusing (even more than my post) because the authors do not use the right terminology. Also, they need to learn the virtues of conciseness and precision. I hope they did not send it to a journal in such bad shape. And of course they assume that all households have the same time preference, against which there is ample evidence and which matters much more as it has strong implications for savings.

What makes people save?

The saving behavior of people is heterogeneous, and what drives it is important for policy. In particular, there is a strong belief that people do not save enough, either because they know the state will bail them out in old age or because their intertemporal preferences are not aligned with the social planner. In any case, what drives people to particular saving behaviors?

Henrik Cronqvist and Stephan Siegel use data from identical twins in Sweden and conclude a little bit over everything is contributing. Of course, results will depend on whether people have faced circumstances that make saving difficult. 35% of the differences in saving propensity can be explained by genes, more so for men, educated and wealthier people. Parental influence is stronger when other siblings are present, which the authors interpret as a situation with less competition for parental resources (why? they are also competing for the parents' attention). But all this means there is still 65% of the variation that can be educated. Which is a lot.

The problem with experimental economics: people are weird

Experimental economics is the up and coming new field, with plenty of interesting research going on. But many people criticize it because the experiments, while controlled, do not reflect real world situations. The stakes in the experiments are too small, the participants are not representative, the experiments not relevant. Add to these criticisms a new one.

Joseph Henrich, Steve Heine and Ara Norenzayan claim this literature, and others, concentrates on Western, Educated, Industrialized, Rich and Democratic (WEIRD) societies. And these societies, with 12% of the world population, are not representative of the rest of the world, in fact they are downright ... weird. To make their point, Henrich, Heine and Norenzayan look, among others, at the ultimatum game in all sorts of societies. This is a standard game that is in particular used to measure altruism ans spite: a prize is to be shared, one player proposes a split, and the other can reject the proposal, leading to both getting nothing. In "weird" societies, the standard result is a 48-50% for the second player, who accepts. Offer below 30% are typically rejected. Subgame perfection, however, would suggest that any amount above zero should be accepted, and the smallest possible is to be accepted.

What about in other societies, the "non-weird" ones? Small scale societies, where people are used to live face-to-face, make low offers and accept them. Worse, it appears that offers that are particularly high are rejected, something that also applies to European societies. Quite consistently, and the authors provide other examples, American experimental subjects end up at an extreme of outcome distributions

Does this mean that the US undergraduate, on whom the vast majority of experiments are performed, is misleading us thoroughly? Not necessarily, after all he may help us understand the homo economicus americanus, but even there one can have doubts. But we may need to rethink seriously how economic theory applies to other societies, at least for some research questions. Experimental economics has still a lot of work on its plate.

C{A|R}RA utility

When it comes to modeling preferences in uncertainty, the usual choice is usually between constant absolute risk aversion (CARA, with an exponential function) and constant relative risk aversion (CRRA, with a power function). That is somewhat limiting, especially when one needs to cover a rather wide domain, as there is then no reason to believe risk aversion remains constant.

Masako Ikefuji, Roger Laeven, Jan Magnus and Chris Muris come up with a mixture, which they name Burr utility. It is CRRA at the origin and CARA at infinity and is a function that has some familiarity for those who use subsistence consumption (Stone-Geary utility function) except that this constant term is added. This implies in particular that marginal utility is never infinite, which is a property I am not sure I want to miss.

Even coercion is Pareto efficient

Much of Economics is based on the premise that property rights are well established, in particular that contracts are either enforced, or self-enforcing. But what if you have to deal with agents with no respect for property and armed with mallets?

Harold Houba and Hans-Peter Weikard describe what they term a "stone age equilibrium." It emerges when exchanges can be coercive, but also voluntary and is thus and intermediate between the Walrasian equilibrium (purely voluntary) and the "jungle equilibrium" of Piccione and Rubinstein (purely coercive). In a stone age equilibrium, no one prefers to take from a weaker agents, and there are no bilateral gains from trade. Interestingly, such an equilibrium can feature gifts and withholding goods. In other words, pretty much anything can happen. Required reading for all doomsday sayers and survivalists.

Earth magnetism and the crisis

I sometimes come across papers, in general from India, that describe how the world could be saved by humanity becoming aware of some philosophical aspect of life, by printing money, or by having a new economic system. Rarely are the writers economists, and rarely do they have functional notions of Economics. But for once, I found a paper that is way off the broken path, yet written by economist and is based on actual science.

Ligia Melo makes the observation that economic outcomes are based on human behavior, and that the latter can be influence by external circumstances. Think about working in extreme climates, where priorities (consumption, comfort, saving) are different from temperate climates. Also, we start to learn from neuroscience, and in particular neuroeconomics, how decision are taken in the brain, and how they may be influenced by external factors.

Melo observes that the magnetic activity of the Earth has markedly decreased, by about 10% in close to two centuries, and the movement of the magnetic poles has increased, currently 41km a year. This is leading to speculations that the magnetic poles could reverse in the foreseeable future. Why would this matter for economic behavior? Magnetic activity has been linked to suicide, mental illness and depression rates. Also, studying human history, people have observed that there has become stronger awareness of individuals (vs. collectivity) in times of low magnetic activity.

Why could this be important? Because most of our decisions are based on expectations, and there is plenty of evidence of self-fulfilling expectations, that is, people for no particular reason expect something to happen, and then it happens. Bubbles, for example. Also, risk-taking behavior is influenced by health, in particular mental health. Melo shows that there is a positive relationship between a country' magnetic field intensity and its GDP. One would want to control with climate factors, but it is intriguing as distance from the equator has consistently been shown as an important factor in growth regressions, even including climate variables.

Physics envy?

As any academic economist who has done consulting fr government or the private sector can testify, it is very difficult to convey the uncertainty about your results and contract givers expect precise numbers with no standard errors. It is particularly ironic that the financial industry, which lives from uncertainty, cannot cope with advice that has qualifications and caveats.

This interpretations does not appear to be shared by Andrew Lo and Mark Mueller, who claim that economists suffer from Physics envy in the sense that they want precise results like in a Physics laboratory. They see part of the reason for the current crisis in the large reliance on quants in the finance industry, and that those quants have neglected that market participants do not behave like electrons, they have feelings and can be unpredictable.

I share with the authors the belief that this crisis is not a failure of Economics and economists, but rather that the expectations of the customers of those economists were way too high. And when those customers were disappointed, they blamed the profession. This rather long paper tries to educate those customers how it really works in Economics, and shows that even Physics is not immune from this unpredictability.