A debt-free stimulus?

Economies may need to be stimulated sometimes, through tax reductions or public expenditures. The problem is that this costs. Opposition to such stimulus programs is typically grounded on the unavoidable debt run-up, which implies that at some point in the future taxes will need to be raised at a level that is higher than before the stimulus. Would there be a way to pacify this opposition?

According to Laurence Seidman there is. It involves the Federal Reserve, or the corresponding central bank, making a loan to the government treasury for the amount engaged in the stimulus, and then the Fed conveniently forgiving this debt. That is a different way of putting what Seidman proposes: the Fed makes simply a transfer to the Treasury that satisfies the dual mandate of the Fed, full employment and stable prices. Despite what Seidman claims, this is monetizing the debt. Even if no debt is explicitly created, the government is still financing its stimulus by (virtually) printing money, and with the same effect on inflation which guarantees that the dual mandate will not be satisfied for stable prices, and one can have doubts about full employment, too. Seidman argues that there would be no inflation if aggregate demand gets back to the "normal" level with the stimulus. But you still have increased the money supply for the same quantity of goods. The price level needs to increase accordingly. The only way to avoid the inflation is if the Treasury returns the transfer to the Fed. The transfer is thus again a debt.

I find it really strange that a chaired professor at the University of Delaware would write this. The only way I can rationalize his writing is that he confuses real and nominal quantities. He also seems to reason in partial equilibrium, not thinking that prices adjust to such large changes in macroeconomic aggregates, especially in the medium run. We are used to seeing this from crackpots with little economics education, but not with apparently well-educated economists.

Capitalism's rapture

Economics is based on a small set of very powerful axioms that a the foundation of utility theory, general equilibrium theory, and more. Experiments have contradicted every one of these axioms one way or the other. We still keep them because they seem to apply most of the time, and the occasional violation does not invalidate the general picture. But it is good to keep an eye on their validity and think about alternative scenarios, especially if they bring us better theories.

Egmont Kakarot-Handtke decides to start afresh with a completely new set of axioms. And instead of choosing some that have some subjectivity, he takes some that are as objective as any axiom could be: four accounting identities and definitions. Yes, you read that right. 1) definition of national product (income approach); 2) a linear production function in labor; 3) definition of nominal consumption as the product of real consumption times a price; and 4) the values of all economic variables this year are last year's variables times one plus their respective growth rate plus an independent and random component for each. Easy. From this Kakarot-Handtke builds an elaborate theory that demonstrates with a mathematical proof (it is in the title, so it must be true) that capitalism is on the verge of collapsing. To me it looks more like his readers could collapse from hyperventilating over this amazing pile of rubbish.

This bizarre scientist has trademarked his models. I am afraid I cannot go into more details about this work without violating some law (Trademark law? Law of sanity?). So I leave it at this.

The best justification for IS-LM?

IS-LM models have always left me puzzled. To me, they are the equivalent to a reduced-form regression with omitted variables and endogeneity issues. Through a lot of hand-waving, you can have any model fit the data. But what I find the most bizarre is this strange obsession with justifying the IS-LM models from micro-foundations. Somehow, IS-LM is taken as an ultimate truth, and one needs to reverse-engineer it to find what can explain it. The ultimate truth is the data, not the model.

Pascal Michaillat and Emmanuel Saez bring us yet another paper that tries to explain the IS-LM model from some set of micro-foundations. The main ones this time are money-in-the-utility-function and wealth-in-the-utility function (and matching frictions on the labor market, which are not objectionable). I find it very hard to believe that by now anybody would consider this a valid starting point. Rarely does anybody enjoy simply having money, the reason why people like having money is that they can buy things with it, things that are already in the utility function, or that money facilitates transactions, something that you can easily model. The same applies to wealth. True, some people may be obsessed with getting richer just for being rich, but for the remainder of the citizen, they like wealth for what it brings in future consumption for themselves and their heirs, and for the security it brings in the face of future shocks. All this easily modelled in standard models.

It seems to me this paper is a serious step back. Macroeconomists try to understand why there are frictions on markets, so that one better determine the impact of policy on such markets. Simply sweeping everything in the utility function, where in addition one has a lot of freedom in choosing its properties, does not help us in any way. And it is wrong, because it is again some sort of reduced form that is not immune to policy changes. Suppose the economic environment becomes more uncertain. Are we now supposed to say that suddenly households like wealth more? They could also like wealth more because of changes in estate taxation or because of longer lifetimes, and these imply very different policy responses in better flushed-out models.

I just do not get it. Maybe some IS-LM fanboys can enlighten me.

About faculty participation in university administration

A major difference between American and other universities is the professionalization of their administration. Typically, they are managed by former faculty who have specialized in higher education administration, and what is become more and more frequent, by administrators who have never been academics. While the result are universities that put in my opinion excessive emphasis on non-academic endeavors like athletics, students living and other student entertainment, there is little doubt that the academics are also in better shape than elsewhere. When faculty are in charge, I suppose there is too much rent seeking. It would be good, though, to have this formalized in some way for better analysis.

Kathleen Carroll, Lisa Dickson and Jane Ruseski build a model of university administration where the extend of faculty involvement may vary exogenously. The model is rather trivial and does not deliver unexpected results, the more faculty participate, the more academic affairs get priority, and this is social optimal if there are externalities from academics to non-academics. What would have really made the paper interesting is to put the model to the data and actually provide some quantification of effects. How much does faculty participation matter? What is the size of cross-effects between academics and non-academics? How big should the administration be? Too bad this paper was only about trivial theory.

Pioneers of the static interest rate

When an author describes his work in the abstract or the introduction, it is common to highlight what is "new," "novel," "unique," an "improvement," or "better." But you do not write that your paper is "pioneering" or "seminal," as this can only be established by others in hindsight.

That does not stop Sarbajit Chaudhuri and Manash Ranjan Gupta, who start their abstract with "This paper makes a pioneering attempt to provide a theory of determination of interest rate in the informal credit market in a less developed economy in terms of a three-sector static deterministic general equilibrium model." OK. So we have a static model to determine the interest rate. That is pioneering. I always thought the interest rate was tied to the relative price of commodities in different periods. I guess the genius here is that with a static model, one needs not to worry about future shocks and even current shocks are instantaneously resolved so the model is also deterministic! This allows to simplify everything to a great extend, but apparently still provides a major improvement of Gupta (1997), that was, however, already pioneering the static determination of the interest rate. So the pioneership of this paper must lie elsewhere. I think the pioneering aspect is rather in the assumption that there is no flow across regional informal markets and moneylenders have a local monopoly. Imagine the pioneering strides we are now making towards a closed-form solution of the model!

The fundamental equation of economics

It is every physicist's dream to find a formula so powerful that it can explain everything (and carry the inventor's name). Such hopes are not as prevalent in Economics, first as we realize that we cannot find such a fundamental equation (we are just not smart enough), second because an economy is so complex that it defies any attempts to reduce it to one equation.

This does not stop James Wayne, who as a physicist is still pursuing his dream. And he claims to have found the Fundamental Equation Of Economics (FEOE), thereby finally proving that Economics is truly part of Physics. What a relief. And what is this equation that can, as the author forcefully argues, explain all observed economic phenomena and solve all economic problems, without exception? What is this formula that shows that equilibrium, the laws of supply and demand, DSGE and SL/ML (whatever that is) models are all deeply flawed? Here it is: The change in time of the joint probability distribution of future valuation of assets and liabilities is a function of its current distribution. We do not know yet what this function is, because it is currently too difficult to figure it out at the atomic level, but we know it exists. Now we can go revolutionize Economics and solve the world's problems.

Long live the Wayne Equation!

Entrepreneurship in Liberia

I have recently mentioned that entrepreneurship cannot be taught, which implies entrepreneurship classes have little value. What should these entrepreneurship professors then do? Do research on entrepreneurship? It turns out that is also of questionable value.

Case in point, the latest paper of Johan Venter. He wants to understand how entrepreneurship emergences in post-conflict economies and lead to new jobs. To this end, he travels to Liberia and surveys ... entrepreneurship professors who, of course, testify about strong interest in entrepreneurship classes. Never mind that taking such classes has no impact on entrepreneurship outcomes, Venter concludes that entrepreneurship should get more emphasis throughout the curriculum. That came out of nowhere, or rather out of a pitiful survey with 28 respondents.

The price of diamonds

The diamond market is really strange. The wholesale market is dominated by a single firm that basically acts like a monopolist, as it is able to control supplies with large stockpiles (and even get a bailout from the government) and thus sets the prices at will. The resale market is heavily self-regulated to limit the supply from others than the dominant firm (just try to resell your diamond and get more than half what you paid for it...). Thus, if you would want to study price setting in this market, you would want to look at it from the angle of a monopolist trying to maximizing its profit and extracting as much as possible from demand.

Nicolas Vaillant and François-Charles Wolff do none of it. They just apply a hedonic regression and blindly regress selling prices on diamond characteristics. It is interesting that they find that there are some important non-linearities at round carat sizes, but is to be expected given the marketing by the diamond industry. But what quickly put me off in this paper is that the authors do not seem to understand some basics of economics. Here is a quote from the first paragraph:

In 2003, world demand for rough diamonds (US$9.5 billion) was significantly above the diamond supply (US$8.2 billion), so that the excess demand had to be satisfied from producers’ existing stockpiles.

First, by definition, supply includes stockpiles. How one would measure supply by ignoring the stock is a mystery to me. And how could one quantify supply and demand separately like this? In addition, it is not like there is rationing going on, which would justify higher demand than supply, as I cannot imagine that a monopolistic supplier set prices below equilibrium. After this opening paragraph, I cannot trust anything in the paper.

PS: And I just realized this is the second time I criticize a paper by these two authors...

There is demand for fresh fruit in Scotland!

When I am thinking about Scotland and the average diet of its residents, I am not thinking about fresh fruit. Indeed, obesity rates there are about the highest anywhere in the world, thanks to a combination of greasy food, high alcohol consumption and general lack of exercise. Fresh fruit does not seem to be high in demand, yet there is a paper that studies the price elasticity of different types of fruit in Scotland.

That paper is by Cesar Revoredo-Giha and Wojciech Florkowski and unfortunately it does not mention any numbers about the level of demand, in particular compared to other regions. The paper, like many papers in agricultural economics has a very narrow focus and it is not clear at all why it would be of interest to anybody outside of Scotland (or even in Scotland, visibly). Is there any lesson to be learned for the rest of us? Anything that could generalize? Some policy implication to get people to eat more healthily? The paper was prepared for a conference in Poland. Why would the paper be of interest there?

Five universal laws for economics

Physicists believe that social sciences can only be described as true sciences if on can figure out some laws that always apply, without exceptions, and if there some invariant constants that would be good, too. Social scientists do not believe this is the right approach, foremost as one has to deal with individuals and societies that make choices.

James Wayne realizes that Physics lacks one ingredient that is essential in social sciences: choice. Fundamentally, I am not convinced that we actually choose, but that it only looks like it at the level of abstraction that we can master at this point and for the foreseeable future. Indeed, our decisions are the results of complex chemical reactions in our brains under the influence of complex environments and likely some randomness. But we have found a simpler abstraction with the framework of choices under constraints, and that is certainly missing in Physics.

Now Wayne adds the concept of choice to Physics, and then determines five new Physics laws: 1) the outcome of any future event is indeterministic; 2) there is a joint probability of future events that helps predicting them; 3) actions can be taken at any time to change this distribution; 4) we cannot retain complete information about past histories; 5) eventually, some equilibrium is reached. He can then use these new laws to re-understand natural and social sciences under a unified framework. All this in only 8 pages of text and not a single equation. True science at work.

The option of suicide

Suicide is a trigger strategy and when to pull the trigger is a decision that involves forming expectations over future outcomes. It is a difficult decision, as future outcomes are very uncertain, if not difficult to quantify.

Shin Ikeda models the suicide decision as the decision to exercise an American option on future wages. Seen this way, the suicide option is straightforward to quantify once you have wage profiles of suicide candidates (to determine timing) and non-candidates (to determine future wage profiles, their distribution and how they may differ form suicide candidates). From anecdotal evidence, anxiety seems to be an important factor, thus modeling at least risk aversion right is very important, as well as bankruptcy. Unfortunately, this is not at all how the paper proceeds. Individuals are risk neutral, but returns are adjusted for market risk. Individuals hold no assets or debt, except their human capital. The wage process is identical for everybody. It is then no surprise that the results are not realistic, indicating that the strike price corresponds to 90% of the average initial wage in perpetuity, meaning that a majority of workers is at suicide risk at some point during their life. Any study in the value of life literature gives numbers much higher than this value, and this is because people value more than just wages. Instead of only looking at money flows, one needs to consider concepts like utility and preferences...

Groom yourself to publish better research?

There is plenty of evidence that being beautiful helps you on the job market. First impressions count a lot, and physical appearance is likely the main factor in first impressions. But does beauty matter in situations where there are no such first impressions? Take the case of scholarly publishing: editors and referees do not see a picture of the author(s), thus it should not matter. If it still matters, it must be that beauty is correlated with something that makes your more likely to get published, say, confidence or more subtly that beautiful people are more healthy, and thus should have had less illness disruptions in schooling and have more human capital. Anyway, we need some evidence.

Alexander Dilger, Laura Lütkenhüner and Harry Müller want to offer some. They asked attendees at a conference of business researchers about their happiness, took their pictures and had others judge these mug shots. They then looked for the publication records of their subjects over the next two years. It turns out that happy people publish more, but of course the causality could run the other way, as you may be happy that your research agenda is progressing well, especially when you are asked about your happiness at a conference in your research field. Maybe more interesting is that a trustworthy appearance is correlated with a better publication record. Is it really the appearance that matters here, or simply that a person who is capable of keeping himself in order is also more likely to be well organized to publish well? Also the population under study (n=49, by the way) is faculty from business schools. It is notorious that in business schools appearances matter a lot, and after law schools it is where it matters the most. Not the kind of sample I would use to make general claims about the research productivity of scholars as it relates to appearance.

homo socialis

Everyone is familiar with homo œconomicus, the greedy economic agent that brings an economy to its most efficient allocation under perfect circumstances. But circumstances are less than perfect (externalities, imperfect competition, lack of commitment, asymmetric information, etc.) and Adam Smith's invisible hand needs a little help from some authority. Through regulation, taxation, subsidies and punishment, that authority can try to get closer to the first-best allocation, but at a cost.

According to Dirk Helbing, this cost is now overwhelming, because in current societies top-down management of an economy is not computable anymore. One should rather find a bottom-up approach, following the craze about Web2.0 and social media. Thus enters homo socialis, an economic agent who is very aware of all the ills of unfettered markets. If this sounds like one of those revolutionary solutions that would end world hunger, it is. It even comes with a new type of money, a must for these types of exercises.

So, how does this work? Homo socialis is an economic agent with other-regarding preferences. He needs institutions that allow him to express such preferences instead for reverting to the greedy homo œconomicus. Hence the institution of "qualified money" that rewards good behavior by this friendly and altruistic market participant by giving him "reputation." But if he is that altruistic, why does he needs such rewards? That is not clear. And who gives them? Is there any budget constraint here? It would really help to formalize a little bit all the author's ideas, but it is quite confusing. For example, the value of qualified money depends on its history. In other words, every single banknote may have a different value, depending on the context in which it was used. How is that simplifying the problem of complexity?

Helbling gives as as example the management of traffic lights in a city, a rather bizarre example. In the homo œconomicus scenario, an authority sets traffic light patterns and does not adapt them when lines become too long somewhere. In the homo socialis, this adaptation happens, presumably from a feedback coming from car drivers. Why the restriction in the first scenario? In fact cities do have feedback rules in place (notice the cameras along the roads?) without the drivers needing to do anything. But foremost, why such an example? It is unrelated to the question at hand. The argument that the computation would be too complex for a central planner fails because at least he has a complete picture. Individual car drivers suffer from a lot of asymmetric information when taking decisions, even altruistic ones. Note also that the example does not use the crazy qualified money scheme.

What a confusing and confused paper. You would think this would be a first draft for someone who works for the first time in the area. But no, except for the methodological silliness and conceptual errors, this paper is actually quite well written and the literature well researched, including 22 self-citations.

Reduced form welfare

It is not uncommon to find theory papers that assume quadratic utility or loss functions. They are the most tractable functions that allow to find an optimum, yet there is no reason to believe they have anything to do with reality. If you are designing an optimal policy where trade-offs are important, the results hinges quite a bit on the functional forms you choose.

Jasper Lukkezen and Coen Teulings look at optimal fiscal policy and go a step further. They attach a VAR (vector autoregression) to a quadratic welfare function. Not only do they assume an analytically tractable but very likely unrealistic welfare function, they also assume the rest of the economy is entirely linear with relationships that are policy invariant (it is a VAR). For their application, welfare is determined over GDP and the unemployment rate, which may be fine to determine the loss function of a policy maker but has nothing to do with the well-being of economic agents. They care about risk, uncertainty, consumption and time off work, all of which are absent from the model. Hence I do not really understand what the results mean, especially as the optimal policy rules are all over the place. A very confusing paper.

Are economists really uneasy about studying inequality?

Economists are often misunderstood. People do not understand what we do. They think we spend all our time forecasting the stock market. And the whole profession has been accused of not foreseeing the recent recession. Some economists have redirected this criticism and made a name for themselves by complaining that economic models do not take this or that into account. That is true, but this is often irrelevant, as models are abstractions and they cannot take everything into account. You want to build the right model for the particular question at hand. I have mentioned a few of those essays on this blog, in part because they frustrate me as they are ignoring the very literature they are calling for. There is a lot more to Economics than the principles with perfect markets we tend to teach as an introduction to the field.

The latest paper to frustrate me is by Brendan Markey‐Towler and John Foster. They claim the Economics profession is uncomfortable with issues about inequality to the point of ignoring them. To support this, they quote extensively from the introduction of the Handbook of Economic Inequality, which of course is going to try to make the case that inequality is underrepresented in the literature. Why so? Markey-Towler and Foster claim this has to do with the profession's adherence to Arrow-Debreu markets, welfare theorems, the Hicks-Kaldor efficiency-equity trade-off, and Arrow's impossibility theorem. Because the profession is so enamored in these theorems, it views the impact of inequality to be political only, but of no economic consequence. Never mind that you can still have inequality in such economies. Never mind that every issue of the top journals has papers with such properties and inequality. Never mind that many papers go through great lengths in trying to model observed inequality while studying many issues. I agree not every paper does this, far from this, but then not every answer hinges on inequality. Again, models are an abstraction, and one cannot include everything. One keeps what is most likely to matter. Occam's razor is still valid today.

Markey-Towler and Foster have this distorted and unfortunately common view that economists believe markets are always complete and perfect, and thus inequality cannot happen. This sounds a lot like those who criticize Economics after taking one class, where they learned that free markets and free trade are good. But economists have long realized that things are much more complicated than that, and the study of the departures from this perfect world dominates current research in Economics. In fact, read this blog and you should see that I hardly mention such perfect markets. The authors' solution? Complex systems theory, which I liken to modeling the actors of an economy being linked by a giant plumbing system with leaks, plugs and bottlenecks. That sounds much like the frictions, information asymmetries and imperfect competition we put in our models, except that complex systems theory is much more detailed, requires gigantic amounts of data to calibrate or estimate, and has gone nowhere so far. So researchers had to resort to heroic assumptions to show something could happen, without any ability to validate it empirically.

I do not think this is the way to go, and we can agree to disagree on that. But I take offense at the idea that economists are somehow uncomfortable, even scared of dealing with inequality. That is just not true.

Is money a factor of production?

An easy trick question to ask students about factors of production is whether money is one. Of course it is not, unless you consider burning it to fuel an oven. A factor of production is an input to the production process, such as capital, labor, raw materials, energy, etc. Money is only a facilitator in the acquisition of those goods. And if money or credit are constraining production, this belongs in a separate constraint, not in the production function.

Why do I mention this? Because money is occasionally put in a production function, and Jonathan Benchimol makes it even the focus and title of his paper. Why does he do that? He wants to estimate a New-Keynesian model and see whether money would matter in such a way. It does not. But who could really blame him for trying, as these models either have money in the utility function (few people enjoy money per se, most people enjoy what you can do with it, and that is already in the utility function) or no money at all (at still manage to draw lessons for monetary policy). In the kingdom of the blind men, those who are blessed with one eye are kings.

How much money laundering is there in Italy?

It is well known that the underground economy in Italy is substantial, and that an important share of this is due to illegal activity. Hence, there should be an important amount of money laundering going on, an amount that seems to be impossible to measure given that these activities precisely try not to get detected. But economists can be resourceful and try to pull it off, for example à la Steve Levitt.

Guerino Ardizzi, Carmelo Petraglia, Massimilano Piacenza, Friedrich Schneider and Gilberto Turati try to pull that off, reasoning that money laundering is performed by depositing cash, and that if there are more cash deposits in financial institutions of an Italian province where there is more activity from illegal syndicates, one should be able to back out how much of these deposits are due to money laundering. Concretely, they regress across provinces over four years cash deposits on a few controls, the number of detected extortion crimes and the number of drug dealing, prostitution and possession of stolen goods. One may have some qualms in using detected crimes, which may be a very poor proxy for actual crime, especially for a country that is so corrupt, but I suppose this is all we have. However, this regression assumes that those illegal syndicates stay within the confines of their province when they deposit their proceeds. Given the size of an Italian province (median inhabitants: 375,000), that seems like a real stretch. I guess we still do not know how much money laundering is going on in Italy.

The obscure economics of vampires

There is a certain appeal to study the economic aspects of something that on first glance has nothing economic. Following the motto of this blog, I have reported on quite a few of those, such as boobs, beer, toilet seats and the scruples of teens. It is almost always good to stretch the boundaries of what we can do with Economics, what I have called the imperialism of Economics. But in rare cases this is going too far.

Daniel Farhat presents us with such a case, wherein he studies the Economics of vampires. The paper uses an agent-based model to follow the interactions of humans and vampires and draws inferences about aggregate phenomena. I have had my issue in the past with agent-based models, in a large part because they are build on unjustified assumptions with no robustness tests, and this papers makes me most concerned about these issues. Indeed, the model is built in a complete empirical vacuum, and none of the modeling assumptions are tested for robustness. Furthermore, because vampires never existed, and with current medical knowledge never will, the paper is pointless.

On the causality between the labor income share and the size of governments

One puzzling feature of national account data in recent years has been a decline in the labor income share across most economies. This is not limited to the last recession but has been happening by and large since the 1970s. Why this is occurring is an important research question, and what the consequences are as well.

François Facchini, Mickael Melki and Andrew Pickering claim that this decrease has lead to a reduction in the size of the government. For this, they build a small two-sector model from which they obtain this positive relationship. Then they run some linear regressions to confirm this. But have they really? With the same model, I can obtain a reverse causation or even both variables being jointly function of others. It all depends on what I am assuming to be exogenous. Invert the regression equation, and you cannot reject the reverse causality either (I suppose, I have not done it). So all they have shown is that there is a correlation, nothing more. Claiming causation here is misleading. And if anything, I would have assumed that the causality runs from government size (which is set by political processes and policy) to the labor income share (which responds to market forces and policy).

How econophysics describes the income distribution

It has been a while since I last discussed a paper from econophysics, where it appears there is a substantial literature trying to describe the distribution of income. It turns out to be quite difficult, because the goal is to do this with a single equation. What one would want to do with that equation is not clear to me, but anyway.

Maciej Jagielski and Ryszard Kutner claim success with this endeavor by essentially dividing up the distribution in three parts, fitting each to a different distribution function, and then rejoining them into a single equation. But what income are they taking about, you may ask? They look at European income in 2006 and 2008, and take the data from the SILC EU project. That still does not determine what income they are considering, as the dataset allows multiple different ways to define income. It is not even clear whether this is income before or after taxes and whether it includes capital gains.

One problem the authors realized is that they need oversampling for to incomes. To take care of this, they look at the European billionaires on the Forbes list of the richest people over several years, conclude that changes in wealth must be "income" and take that, dropping all negative incomes along the way. Then they notice a large discontinuity from merging the two dataset and decide to divide the top incomes by 100 to make the joint distribution continuous. Oh boy. And this is the dataset they used for their study, believe it or not.