Throughout the last crisis, there has been much talk about excessive borrowing by individuals (and countries), and how this seems to follow some irrational behavior. We have to understand here that irrationality is a very strong concept, in the sense that people would knowingly take decisions that are against their best interest. I am not saying this does not happen, but ignorance and wrong beliefs can lead to behavior that looks irrational but is in fact perfectly rational.
Ari Hyytinen and Hanna Putkuri explore some data from Finland and find that those who borrow excessively do so because they are much more optimistic about future outcomes. Believing that future incomes will be high seems a perfectly rational justification for borrowing, especially when the lender seems to share this assessment. What is more worrisome though is that those overly optimistic households have more difficulties revising their expectations when faced with evidence. Maybe they hate it to be proven wrong (who does not?). Maybe it is Finnish bankruptcy law that encourages them to go for broke once a point of no return is reached. The latter would be perfectly rational again.
Showing posts with label fundamentals. Show all posts
Showing posts with label fundamentals. Show all posts
Put some economics back into spatial econometrics
One of the hot areas for econometric research in recent years has been spatial econometrics. Think of it, at least initially as time series econometrics in a different dimension. One interesting aspect of it is that instead of being single-dimensional like time series, it can be two-dimensional, or even more I guess. This field brings interesting new challenges, and it must be exciting working in this field. However, as too often in econometric theory, research becomes quickly detached from reality, and more specifically from the needs of empiricists. N never goes to infinity, for example.
Luisa Corrado and Bernard Fingleton bring forward another important point. These techniques are used to test economic theories, so one should be able to embed some restrictions from economic theory. It is all nice and sweet when one can find an optimal weighting matrix with the right properties, but it is useless if the found weights cannot be matched with anything one wants to test. The causality goes the wrong way: first determine restrictions from the theory, then use the constraints to find the optimal weighting matrix.
This is not just a theoretical consideration. Spatial lags are crucial in spatial econometrics and are suppose to capture some network effects. But they can also soak up the impact of latent or unobserved variables, as in "regular" econometrics. This can lead to severe miss-specification and biased inference, somethings one is all to familiar with using lags in time series. In fact, one should be downright suspicious of any time-series results that only holds when lagged dependent variables are used. The same must apply to spatial econometrics.
Luisa Corrado and Bernard Fingleton bring forward another important point. These techniques are used to test economic theories, so one should be able to embed some restrictions from economic theory. It is all nice and sweet when one can find an optimal weighting matrix with the right properties, but it is useless if the found weights cannot be matched with anything one wants to test. The causality goes the wrong way: first determine restrictions from the theory, then use the constraints to find the optimal weighting matrix.
This is not just a theoretical consideration. Spatial lags are crucial in spatial econometrics and are suppose to capture some network effects. But they can also soak up the impact of latent or unobserved variables, as in "regular" econometrics. This can lead to severe miss-specification and biased inference, somethings one is all to familiar with using lags in time series. In fact, one should be downright suspicious of any time-series results that only holds when lagged dependent variables are used. The same must apply to spatial econometrics.
How group utility differs from individual utility
Expected utility lies at the core of almost all analyses in Economics that feature uncertainty. While one can always come up with exceptions to the rule, expected utility is by and large accepted as a good characterization of the behavior under uncertainty of individuals. The emphasis is here on individuals, and one can wonder whether the behavior could be different when they act as a group. Group dynamics are complex, and there are plenty of examples where individuals behave differently in a group than alone.
Andrea and Piergiuseppe Morone performed an experiment with students where they tried to elicit responses to risky choices, first individually, then in random groups of two. They confirm that in the first case, expected utility seems to be the best representation of the preferences, but in the second case disappointment aversion (commonly also called loss aversion) seems to be dominating. But this is far from being a proof yet, as only 38 students were part of the experiment. The authors also claim that this shows that preference aggregation drives out expected utility, a statement that can be misinterpreted. Indeed, this does not mean that aggregating individual decisions leads to a rejection of expected utility (say, in a representative agent framework). It only says that in situations where people take joint decisions, expected utility may be dominated.
Andrea and Piergiuseppe Morone performed an experiment with students where they tried to elicit responses to risky choices, first individually, then in random groups of two. They confirm that in the first case, expected utility seems to be the best representation of the preferences, but in the second case disappointment aversion (commonly also called loss aversion) seems to be dominating. But this is far from being a proof yet, as only 38 students were part of the experiment. The authors also claim that this shows that preference aggregation drives out expected utility, a statement that can be misinterpreted. Indeed, this does not mean that aggregating individual decisions leads to a rejection of expected utility (say, in a representative agent framework). It only says that in situations where people take joint decisions, expected utility may be dominated.
Being tall and risk aversion
Are tall people less risk-averse than others? That seems like an odd question to ask. In particular, what would one want to do with the answer? Well, if the answer is no, this can help in establishing better insurance policies. Also, if some policies influence height, it is good to know what taller people entails (besides the known higher income and confidence).
Olaf Hübler finds that, yes, tall people are less risk averse. What is more interesting is that this result disappears once you factor in other variables, like personality, skills, and information about parental behavior. That is particularly interesting because such information is often not readily available, whereas height can be easier to find and used as a proxy. So, not such an odd question after all. Another question is then why would there be such a relationship.
Olaf Hübler finds that, yes, tall people are less risk averse. What is more interesting is that this result disappears once you factor in other variables, like personality, skills, and information about parental behavior. That is particularly interesting because such information is often not readily available, whereas height can be easier to find and used as a proxy. So, not such an odd question after all. Another question is then why would there be such a relationship.
Econochemistry?
I have highlighted in the past some exceptionally bad examples of forays of physicists into Economics (search for "Econophysics"). Not all are that bad, but they generally have in common that they portray the economy just as exogenous stochastic processes where no economic agents take decisions. That can make sense in some contexts, but these are rare cases. Now it seems chemists are venturing into Economics, what good could that bring?
Yochanan Shachmurove and Reuel Shinnar are an economist and a chemist who managed to get a paper into the working paper series of the department of Economics at the University of Pennsylvania. So it must be a serious piece. Their point is that is Chemistry, they have to deal with chemical reactors that depend on many variables and are very difficult to predict. This is not unlike an economy, where a multitude of factors may matter in ways so complex with some randomness thrown in that forecasting is very difficult as well. The authors suggest to use partial control, which involves identifying a few crucial variables and monitor those for forecasting. That does not look like much of an innovation to economists, as we are used to abstract modeling, factor analysis, econometrics and simple rules like the Phillips Curve or the Taylor Rule.
The methodology of partial control they are trying to push, though, hits a few roadblocks when applied to Economics. The first is that it needs an objective, which is easy to set in a chemical plant (it is the choice of the plant manager) but not so easy for an economy as a whole. The second is that it needs to separate the problem into independent units. They suggest, for example, to treat the United States as independent from the rest of the world. That may work for some questions, but many it does not, especially when the point is to summarize complex interactions. Third, the procedure requires a hierarchy of reactions. Much of our understanding of general equilibrium would not be captured by such constraints. Fourth, the method relies a lot on the ability to manipulate controls. That is easy in a chemical plant, but an entirely different problem in an economy. The authors take the example of the Fed and interest rates. Well, the Fed has a target on one very special interest rate, all others are market driven.
While Shachmurove and Shinnar offer scattered examples of how to apply partial control, there is no sense of how a complete model would look like. I would wait to see a model in operation before calling this an interesting modeling strategy for Economics.
Yochanan Shachmurove and Reuel Shinnar are an economist and a chemist who managed to get a paper into the working paper series of the department of Economics at the University of Pennsylvania. So it must be a serious piece. Their point is that is Chemistry, they have to deal with chemical reactors that depend on many variables and are very difficult to predict. This is not unlike an economy, where a multitude of factors may matter in ways so complex with some randomness thrown in that forecasting is very difficult as well. The authors suggest to use partial control, which involves identifying a few crucial variables and monitor those for forecasting. That does not look like much of an innovation to economists, as we are used to abstract modeling, factor analysis, econometrics and simple rules like the Phillips Curve or the Taylor Rule.
The methodology of partial control they are trying to push, though, hits a few roadblocks when applied to Economics. The first is that it needs an objective, which is easy to set in a chemical plant (it is the choice of the plant manager) but not so easy for an economy as a whole. The second is that it needs to separate the problem into independent units. They suggest, for example, to treat the United States as independent from the rest of the world. That may work for some questions, but many it does not, especially when the point is to summarize complex interactions. Third, the procedure requires a hierarchy of reactions. Much of our understanding of general equilibrium would not be captured by such constraints. Fourth, the method relies a lot on the ability to manipulate controls. That is easy in a chemical plant, but an entirely different problem in an economy. The authors take the example of the Fed and interest rates. Well, the Fed has a target on one very special interest rate, all others are market driven.
While Shachmurove and Shinnar offer scattered examples of how to apply partial control, there is no sense of how a complete model would look like. I would wait to see a model in operation before calling this an interesting modeling strategy for Economics.
Rational expectations as an optimal approximation
The rational expectations hypothesis has somehow fallen into disrepute because it is viewed as somehow failing to predict or account for the recent crisis. This is of course because of a fundamental misunderstanding of the hypothesis, as it does not imply that markets are efficient, or in equilibrium, or that the equilibrium is unique, or that bubbles cannot happen. But it is a hypothesis, and as any hypothesis it could be rejected by the evidence, for example by experiments showing people are afraid of Knightian uncertainty and yet are optimistic. But the fact that there was a crisis is not empirical evidence in this regard.
Kenneth Kasa actually shows that the rational expectations hypothesis is also a result, up to a close approximation. Indeed, if one is uncertain about the economic environment ("does not know the model"), one adheres to robust rules in the sense that among all possible models, one picks the one with the worst possible outcome. Call this the 'evil' agent. Add to this the assumption that one likes being optimistic. Call this the 'angelic' agent. Now assume that the evil and the angelic agents negotiate what to do in a Nash sense. They will then choose to behave in a way that is very close to rational expectations. This means that optimal expectations are rational, even though the model is not known and one has the documented psychological biases. In other words, rational expectations can be a useful approximation even outside the usual core of assumptions.
Kenneth Kasa actually shows that the rational expectations hypothesis is also a result, up to a close approximation. Indeed, if one is uncertain about the economic environment ("does not know the model"), one adheres to robust rules in the sense that among all possible models, one picks the one with the worst possible outcome. Call this the 'evil' agent. Add to this the assumption that one likes being optimistic. Call this the 'angelic' agent. Now assume that the evil and the angelic agents negotiate what to do in a Nash sense. They will then choose to behave in a way that is very close to rational expectations. This means that optimal expectations are rational, even though the model is not known and one has the documented psychological biases. In other words, rational expectations can be a useful approximation even outside the usual core of assumptions.
An economist's foray into econophysics
I have described here some of the outlandish forays of physicists into Economics, where they try to use concepts from their discipline with disastrous results (last post here). But economists themselves may borrow concepts from physics. One that stuck was sunspots, from some chance correlation between sunspot activity and stock market performance, and made popular by David Cass and Karl Shell. But there were very little physics in this.
Martin Evans ventures deeper, using the concept of dark matter to understand exchange rate movements. It is well known that it is very difficult to understand what moves exchange rates. Dark matter is something that we cannot observe, but we see its impact. In this case, Evans builds a model where dark matter has an impact on nominal exchange rates, and then on other variables. This is based on the empirical observation that something like dark matter has an impact on expectations on long-run exchange rates, but not on recent and future interest rate differentials. This is achieved in the model by introducing shocks to household risk aversion. Why? Well, it is dark matter (or animal spirits). But all that matters is that it explains a large share of exchange rate fluctuations, in a rather consistent way for the other variables. Physicists would be happy with that. Economists would want to understand why.
Martin Evans ventures deeper, using the concept of dark matter to understand exchange rate movements. It is well known that it is very difficult to understand what moves exchange rates. Dark matter is something that we cannot observe, but we see its impact. In this case, Evans builds a model where dark matter has an impact on nominal exchange rates, and then on other variables. This is based on the empirical observation that something like dark matter has an impact on expectations on long-run exchange rates, but not on recent and future interest rate differentials. This is achieved in the model by introducing shocks to household risk aversion. Why? Well, it is dark matter (or animal spirits). But all that matters is that it explains a large share of exchange rate fluctuations, in a rather consistent way for the other variables. Physicists would be happy with that. Economists would want to understand why.
We all have self-centered beliefs about others
It should be no surprise that people tend to think they are better than average, or at least that they think they are better than they truly are. The only possible exception I can think to this optimism are Southern European who seem to constantly self-deprecate themselves. In some ways this is disturbing news, for example when we think about rational expectations: our theories say people should not be making systematic mistakes.
Eugenio Proto and Daniel Sgroi go even further. They find that people perceive the distribution of the others to depend on their own place in the true distribution. In probably better words: If you are in the tails of the true distribution, you perceive to be closer to the center than reality. I think the most likely explanation for this has to do with the fact that we do not interact with the complete distribution of people, but most likely those that are most like oneself. I tend to interact mostly with highly educated people, because of my profession, and thus I tend to think most people are well educated, notwithstanding my disappointments when teaching undergraduates.
With respect to rational expectations, the next questions is whether these personal biases cancel out in the aggregate. Possibly, but this misses the true concern. While most aggregates may not be affected by this kind of bias, the distribution of outcomes may, and this could even affect some aggregates (say, savings, human capital). How much? I really do not know.
Eugenio Proto and Daniel Sgroi go even further. They find that people perceive the distribution of the others to depend on their own place in the true distribution. In probably better words: If you are in the tails of the true distribution, you perceive to be closer to the center than reality. I think the most likely explanation for this has to do with the fact that we do not interact with the complete distribution of people, but most likely those that are most like oneself. I tend to interact mostly with highly educated people, because of my profession, and thus I tend to think most people are well educated, notwithstanding my disappointments when teaching undergraduates.
With respect to rational expectations, the next questions is whether these personal biases cancel out in the aggregate. Possibly, but this misses the true concern. While most aggregates may not be affected by this kind of bias, the distribution of outcomes may, and this could even affect some aggregates (say, savings, human capital). How much? I really do not know.
About the cult of statistical significance
A large part of economic research is devoted to empirical studies, and the name of the game there is statistical significance. Once you get an interesting effect being significant, it becomes a study worth writing, and possibly publishing. If you cannot find an effect, then try another specification until it is statistically significant. Nobody will know how much you tried, the one that is significant is all that counts. If one cannot find a statistically significant result, a null result, publishing it becomes really difficult. This game of finding statistical significance is unfortunately misleading, as this hunt dominates theory or even common sense when choosing specifications, and often completely neglects economic significance. What if a statistically significant results is tiny even though it is precise? And what about a large effect that is statistically weak?
I am certainly guilty as well of confusing statistical and economic significance, including on this blog. Indeed, it is often difficult even understanding what the size of the effect is, because the specification does not allow one to relate the effect to something tangible.
The reason I am mentioning all this is that I came across a recent paper by Walter Krämer, who tries to revise the argument made by Stephen Ziliak and Deirdre McCloskey that any statistical significance is useless. While he seems to concur on the general abuse of statistical significance, he claims it can still be useful under some circumstances. And these are when you do exploratory testing, as it allows to discard unviable hypotheses or specifications. But one has to remember, even though this is elementary statistics yet so often ignored, one can only reject or not reject, but never accept a hypothesis. So even the exploratory testing has its limitations.
I am certainly guilty as well of confusing statistical and economic significance, including on this blog. Indeed, it is often difficult even understanding what the size of the effect is, because the specification does not allow one to relate the effect to something tangible.
The reason I am mentioning all this is that I came across a recent paper by Walter Krämer, who tries to revise the argument made by Stephen Ziliak and Deirdre McCloskey that any statistical significance is useless. While he seems to concur on the general abuse of statistical significance, he claims it can still be useful under some circumstances. And these are when you do exploratory testing, as it allows to discard unviable hypotheses or specifications. But one has to remember, even though this is elementary statistics yet so often ignored, one can only reject or not reject, but never accept a hypothesis. So even the exploratory testing has its limitations.
Economists lie more
Economists have a bad reputation, and this does not date from the recent crisis. This comes largely from those willing to prostitute themselves for various think tanks, lobby groups or trade group, or their employer, acting like a spokesperson than a critically-thinking and independent economist. Any many of those call themselves economists without actually being one (hence my earlier call for a certification). In the end, the general public thinks we are lying, mostly. Is there any truth to this?
Raúl López-Pérez and Eli Spiegelman performed some experiments where lying could be a winning strategy. While religiosity or gender does not impact the propensity of lying, one's undergraduate major matters, and the Business and Economics students fare the worst. And this does not come from selection into the major, it is acquired. So economists do lie more, because they see incentives to do so. Hence journalists should learn to interview economists that have no incentives to lie.
Somewhat relatedly, economists are also less generous, but in that case it is not acquired, but rather stemming from selection. But in both cases, it shows economists conform more to homo oeconomicus,
Raúl López-Pérez and Eli Spiegelman performed some experiments where lying could be a winning strategy. While religiosity or gender does not impact the propensity of lying, one's undergraduate major matters, and the Business and Economics students fare the worst. And this does not come from selection into the major, it is acquired. So economists do lie more, because they see incentives to do so. Hence journalists should learn to interview economists that have no incentives to lie.
Somewhat relatedly, economists are also less generous, but in that case it is not acquired, but rather stemming from selection. But in both cases, it shows economists conform more to homo oeconomicus,
Precaution versus prudence
Why do people accumulate precautionary savings. Conventional wisdom tells us this happens because people face some shocks to income and they are averse to risk and prudent. Now, we need to be careful here. Risk aversion means that one does not like fluctuations in utility (say, from consumption). Prudence means that one dislikes bad outcomes. Hence they do not mean exactly the same thing, and one could conceive precautionary savings without prudence.
Agustín Roitman shows an example where this works. To do this, he uses a new class of preferences that allows to distinguish clearly risk-aversion and prudence: act - bct3 (it may not be easily visible, this is linear consumption less cubed consumption with some coefficients). It has a relative coefficient of prudence of -1 (ratio of third to second derivative), which is constant and independent of risk aversion. The negative value also means this economic agent is imprudent. As Roitman shows, this agent will still accumulate precautionary savings, hence prudence is not necessary. And except for these assumptions on the utility function, the results holds quite generally.
Agustín Roitman shows an example where this works. To do this, he uses a new class of preferences that allows to distinguish clearly risk-aversion and prudence: act - bct3 (it may not be easily visible, this is linear consumption less cubed consumption with some coefficients). It has a relative coefficient of prudence of -1 (ratio of third to second derivative), which is constant and independent of risk aversion. The negative value also means this economic agent is imprudent. As Roitman shows, this agent will still accumulate precautionary savings, hence prudence is not necessary. And except for these assumptions on the utility function, the results holds quite generally.
Measuring optimists and pessimists
According to Wikipedia, the Oxford English Dictionary defines optimism as "hopefulness and confidence about the future or successful outcome of something; a tendency to take a favourable or hopeful view." This is a poor definition, I think, because it mixes two concepts: a) that one has subjective probabilities on good outcomes higher than what objective ones would warrant, 2) that one is less sensitive to uncertainty. Put it that way, it becomes quite obvious that economists have the tools to separate the two and can thus document what I would call true optimism (or pessimism), a tendency for favor good outcomes in expected utility.
David Dillenberger, Andrew Postlewaite, and Kareen Rozen do exactly this by appealing to Savage's subjective expected utility. If we look at choices of an individual across lotteries, then we should be able to back out both the subjective probability distribution and the aversion to risk. This can actually be a big deal, for example when one wants to give advice in a risky environment. The advisor will try to understand the preferences of the subject, but ignoring optimism/pessimism may lead to a very erroneous assessment of risk aversion. This can be crucial in assessing, say, investment strategies, health care options, or career choices.
David Dillenberger, Andrew Postlewaite, and Kareen Rozen do exactly this by appealing to Savage's subjective expected utility. If we look at choices of an individual across lotteries, then we should be able to back out both the subjective probability distribution and the aversion to risk. This can actually be a big deal, for example when one wants to give advice in a risky environment. The advisor will try to understand the preferences of the subject, but ignoring optimism/pessimism may lead to a very erroneous assessment of risk aversion. This can be crucial in assessing, say, investment strategies, health care options, or career choices.
Non-conformism in academia
In economics, there is the mainstream and various heterodox fractions that do not identify with the mainstream or each other. Too many, the heterodox seem like annoying and useless chatter. But they fulfill an important role, which is to keep those in the mainstream on their toes. This is part of the scientific process: challenge long and widely held views to check whether they are still valid. And even if the challenge is wrong, it makes those in the mainstream think about the axioms the build their theories on.
Vela Velupillai makes the case that dissenters are typically silenced by the mainstream, but may prevail in the long-run, citing many examples of mathematical economics, in particular the work of Pietro Sraffa. And this is really what tenure is good for: a dissenter will have much trouble publishing but may ultimately still contribute a lot to scientific advances.
We are in times were dissenters seems to have a more receptive audience. Indeed, the current crisis is seen by some as a failure of Economics, thus it is easy to criticize it. But it also highlights that dissenting can be very distracting if it is poorly focused, uniformed and populist. In this regard the recent dissenting by Colander, Krugman and Stiglitz has, I think been counterproductive, as I have occasionally discussed here. It is good to criticize the fundamental assumptions of the mainstream. It is better, but not necessary to offer alternatives. But it is counterproductive to dissent on the basis of a old read of the literature, and a literature that has in the meanwhile evolved to address these supposedly new criticisms.
Heterodox Economics has thus gained a fresh wind, simply because it is different from the mainstream. But what does it have to offer? Peter Skott, a heterodox economist himself, argues that it is still far from being an viable alternative. While heterodox approaches usually reject microeconomic foundations in macroeconomics, they should not throw out the baby with the bath water, i.e., ignore microeconomics altogether. And while the heterodox analysis for income distribution has focused on the labor income share, the large changes in income inequality happened within labor income. Finally, the heterodox literature is just as guilty of ignoring many of the suddenly relevant intricacies of the world of finance.
There is still a lot of work to do, and instead of pursuing an excess of mutual accusations and claiming Economics is useless, it seems much more appropriate to put more resources into making it better, heterodox or mainstream. After all, medical research was not defunded when the HIV/AIDS epidemic made ravages.
Vela Velupillai makes the case that dissenters are typically silenced by the mainstream, but may prevail in the long-run, citing many examples of mathematical economics, in particular the work of Pietro Sraffa. And this is really what tenure is good for: a dissenter will have much trouble publishing but may ultimately still contribute a lot to scientific advances.
We are in times were dissenters seems to have a more receptive audience. Indeed, the current crisis is seen by some as a failure of Economics, thus it is easy to criticize it. But it also highlights that dissenting can be very distracting if it is poorly focused, uniformed and populist. In this regard the recent dissenting by Colander, Krugman and Stiglitz has, I think been counterproductive, as I have occasionally discussed here. It is good to criticize the fundamental assumptions of the mainstream. It is better, but not necessary to offer alternatives. But it is counterproductive to dissent on the basis of a old read of the literature, and a literature that has in the meanwhile evolved to address these supposedly new criticisms.
Heterodox Economics has thus gained a fresh wind, simply because it is different from the mainstream. But what does it have to offer? Peter Skott, a heterodox economist himself, argues that it is still far from being an viable alternative. While heterodox approaches usually reject microeconomic foundations in macroeconomics, they should not throw out the baby with the bath water, i.e., ignore microeconomics altogether. And while the heterodox analysis for income distribution has focused on the labor income share, the large changes in income inequality happened within labor income. Finally, the heterodox literature is just as guilty of ignoring many of the suddenly relevant intricacies of the world of finance.
There is still a lot of work to do, and instead of pursuing an excess of mutual accusations and claiming Economics is useless, it seems much more appropriate to put more resources into making it better, heterodox or mainstream. After all, medical research was not defunded when the HIV/AIDS epidemic made ravages.
Adaptive versus rational expectations
There was a time where macroeconomics was ruled by adaptive (or backward-looking) expectations, like the much-ridiculed chartists. Then there was a revolution and rational (typically forward-looking) expectations were widely adopted, realizing that people are not stupid and will try to use the available information, including what other agents may do, to figure out what the future holds. Rationality, and in particular rational expectations, has recently come under attack because models failed to predict recent bubbles and crashes. I think this is mistaken, as detailed on several occasions on this blog.
Gregory Chow, however, longs for a return to adaptive expectations for three other reasons. The first is that it is empirically more plausible. Exhibit A is a regression of the stock prices of 50 blue chips in Taiwan on current dividends and past dividend growth. Despite a lowly R2 of 0.111, the fact that the coefficient on dividend growth is positive and significant is taken as evidence of adaptive expectations. I do not find this convincing, as a similar result could emerge with rational expectations if the dividend growth process is persistent.
The second reason is that "there is no reason to believe that the expected values [computed from an econometric model of the rational expectations] will have a sum, after discounting, which equals the actual current price." I think the underlying reasoning is that a statistician can only use a linear combination of past observations, thus economic agents will, too, and this all looks like adaptive expectations. But economic agents, and nowadays statisticians, are more sophisticated than that, and a gigantic literature in finance has shown that, for example, non-linearities and endogenous volatility, too name a few, are important. Even though statisticians have become much more sophisticated, they are still running behind economic agents and are far removed from being linearly backward-looking.
The third reason is that macroeconomists started using rational expectations simply because it was required to deal with the Lucas Critique, empirical evidence be damned. While I can be sympathetic to the argument that rational expectations was adopted without much direct empirical evidence, I also believe that economic agents do try and avoid systematic mistakes and that their expectations contain at least some rationality. And as much literature has shown, a modicum of rationality can bring markets darn close to prices that look like perfectly rational ones.
Gregory Chow, however, longs for a return to adaptive expectations for three other reasons. The first is that it is empirically more plausible. Exhibit A is a regression of the stock prices of 50 blue chips in Taiwan on current dividends and past dividend growth. Despite a lowly R2 of 0.111, the fact that the coefficient on dividend growth is positive and significant is taken as evidence of adaptive expectations. I do not find this convincing, as a similar result could emerge with rational expectations if the dividend growth process is persistent.
The second reason is that "there is no reason to believe that the expected values [computed from an econometric model of the rational expectations] will have a sum, after discounting, which equals the actual current price." I think the underlying reasoning is that a statistician can only use a linear combination of past observations, thus economic agents will, too, and this all looks like adaptive expectations. But economic agents, and nowadays statisticians, are more sophisticated than that, and a gigantic literature in finance has shown that, for example, non-linearities and endogenous volatility, too name a few, are important. Even though statisticians have become much more sophisticated, they are still running behind economic agents and are far removed from being linearly backward-looking.
The third reason is that macroeconomists started using rational expectations simply because it was required to deal with the Lucas Critique, empirical evidence be damned. While I can be sympathetic to the argument that rational expectations was adopted without much direct empirical evidence, I also believe that economic agents do try and avoid systematic mistakes and that their expectations contain at least some rationality. And as much literature has shown, a modicum of rationality can bring markets darn close to prices that look like perfectly rational ones.
Politicians and leisure
When you think about leisure in the utility function, for most applications you need to take a stand on some properties: is the income effect larger than the substitution effect? Is leisure a normal good in the first place? Convincing empirical evidence is surprisingly difficult to find: read the endless debate between microeconomists and macroeconomists about the size of the wage elasticity. This may be an aggregation issue, but maybe we are lacking a clear natural experiment.
Naci Mocan and Duha Altindag report on an interesting change in the way members are paid in the European parliament. Whereas previously they were compensated at wildly different levels by there home countries, since July 2009 they get money according to a uniform rule: 38.5% of a European judge's salary as a base, plus a per diem when present. Mocan and Altindag then use the difference between and with the previous schemes to highlight that an increase in the base reduces attendance (yes, the income effect! Leisure is normal!) and an increase in the per diem increases attendance (the substitution effect is larger than the income effect). Politicians are rational after all.
Naci Mocan and Duha Altindag report on an interesting change in the way members are paid in the European parliament. Whereas previously they were compensated at wildly different levels by there home countries, since July 2009 they get money according to a uniform rule: 38.5% of a European judge's salary as a base, plus a per diem when present. Mocan and Altindag then use the difference between and with the previous schemes to highlight that an increase in the base reduces attendance (yes, the income effect! Leisure is normal!) and an increase in the per diem increases attendance (the substitution effect is larger than the income effect). Politicians are rational after all.
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.
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.
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.
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.
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.
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.
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