Thursday, May 25, 2017

Scale invariance and wealth distributions

In a conversation with Steve Roth, I recalled a paper I'd read a long time ago about wealth distribution:
We introduce a simple model of economy, where the time evolution is described by an equation capturing both exchange between individuals and random speculative trading, in such a way that the fundamental symmetry of the economy under an arbitrary change of monetary units is insured.
That's how econophysicists Bouchaud and Mezard open their abstract. Their approach is a good example of an effective field theory approach (write down the simplest equation that obeys the symmetries of the system). But interestingly, the symmetry they chose is exactly the same scale invariance that leads to the information equilibrium condition (see here or here). I hadn't payed much attention to this line before, but now it has more significance for me. The scale invariance is also related to money: money is anything that helps the scale invariance hold.

The equation Bouchaud and Mezard write down simply couples (creates a nexus between) wealth of each agent and some field that exhibits Brownian motion with drift (i.e. a stock market). It also couples the wealth of agents to each other (i.e. exchange):

As you can see, taking W → α W leaves this equation unchanged.

This scale invariance is probably what allows their model to generate wealth distributions with Pareto (power law) tails.

Wednesday, May 24, 2017

More on Hayek and information theory

My piece at Evonomics was largely well-received in the econoblogosphere. The exception should be obvious: fans of Hayek. Actually, my editor and I discussed the likely backlash before publication.

The most common complaint was some sort of knee-jerk complaint response fans of Hayek seem to have: "you haven't read the vast literature of Hayek". It was pretty strange to me because I've actually read a bunch of Hayek's writing. Only a limited part of it is relevant to the microeconomics in my Evonomics piece.

Hayek wrote about (among other things, so do not consider this list exhaustive):

  1. The price mechanism (e.g. The Use of Knowledge in Society)
  2. Intertemporal equilibrium (e.g. Economics and Knowledge)
  3. Business cycles (includes his arguments with Keynes)
  4. The central planning calculation problem (his expansions on Mises, including The Use of Knowledge in Society)
  5. The political effects of central planning (e.g. The Road to Serfdom)

As no one really understands business cycles (the identity and cause of recessions represent an open question in macroeconomics), any contribution to item 3 is only meaningful if it represents a useful description of empirical data. Hayek is a bit on the wordy side, and doesn't really engage with data.

Item 4 is generally true at the time of Hayek and probably for hundreds of years in the future as Cosma Shalizi shows in his excellent book review of Red Plenty:
There are many, many things to be said against the market system, but it is a mechanism for providing feedback from users to producers, and for propagating that feedback through the whole economy, without anyone having to explicitly track that information. This is a point which both Hayek, and Lange (before the war) got very much right. The feedback needn’t be just or even mainly through prices; quantities (especially inventories) can sometimes work just as well. But what sells and what doesn’t is the essential feedback. 
It’s worth mentioning that this is a point which Trotsky got right.
However, while Hayek might have intuitively understood the issue, you can't demonstrate it in any convincing way without understanding computational complexity (as Shalizi also shows). Just asserting the calculation problem is too hard to solve isn't quite the same as showing that the linear programming problem would take a massive amount of computational resources. And truthfully, the linear programming problem is actually solvable, so some future society could eventually implement it (meaning Mises and Hayek were correct, but only for a period of time).

The point Shalizi also makes is that because the problem is too complex to solve without a heroic dose of computational resources, you can't actually know if the market's "heuristic" solution is optimal. It's just "a" solution.

Item 5 is a political treatise, and, empirically speaking, a largely false one as e.g. the United Kingdom hasn't devolved into totalitarianism in the intervening decades despite running a mixed economy. I recently had a discussion about this with some colleagues that were fans of Hayek who backtracked to the position that Hayek was only talking about something that "could happen". However even that is incorrect as Hayek said that "tyranny ... inevitably results from government control of economic decision-making" (emphasis mine).

This leaves items 1 and 2.

Coincidentally, David Glasner posted a pretty good rundown of item 2 earlier this week. I also discuss the concept of intertemporal equilibrium (and its potential failure) using information equilibrium in many contexts on this blog (including framing the problem as information transfer from the future to the present, statistical equilibrium, and dynamic equilibrium). The information equilibrium approach is fully consistent with Hayek in the sense that, as Glasner put it:
[Hayek believed] he had in fact grasped the general outlines of a solution when in fact he had only perceived some aspects of the solution and offering seriously inappropriate policy recommendations based on that seriously incomplete understanding.
In that sense, information equilibrium can be seen as offering a potential framework for addressing the intertemporal equilibrium problem Hayek identified. But I didn't discuss this in the article.

While none of items 2-5 are really discussed in the article (item 2 is alluded to with a comment about future and past distributions, and I actually agree with Hayek on item 4 but only allude to it with a link to Shalizi's blog post linked above), many of the Hayek fans brought them up in comments at Evonomics or on Twitter saying that I misunderstood them or failed to talk about them. 

I'll freely admit I failed to talk about them (and it's hard to say if someone misunderstands things they don't talk about). My Evonomics article concentrated on item 1: the price mechanism. I tried to explain what Hayek got wrong about it, what he got right, and how we might understand it in terms of information theory. Information theory naturally leads to serious arguments against assuming the efficacy of the market mechanism, so that where Hayek is enthralled with how well it works, we should instead by surprised -- and on the look-out for some non-market mechanism in place propping it up.

I actually don't claim Hayek got many things wrong. The title is "Hayek Meets Information Theory. And Fails." Generally titles for pieces are created by the editors, and this case was no different. However, I did approve it so I am at least partially responsible for it. And given the arguments in the article, this title is not far off the mark: Hayek's description of the price mechanism as a communication system is not consistent with information theory.

The only claims I make about Hayek in the article are:
1. Friedrich Hayek did have some insight into prices having something to do with information, but he got the details wrong and vastly understated the complexity of the system.
[Several readers took this to mean that I said Hayek said markets weren't complex. If you read that carefully, you'll notice that I only said Hayek understated the complexity.]
2. Hayek thought a large amount of knowledge about biological or ecological systems, population, and social systems could be communicated by a single number: a price.
[This is the statement behind the title that I go into more detail about below.]
3. Ideas that were posited as articles of faith or created through incomplete arguments by Hayek are not even close to the whole story, and leave you with no knowledge of the ways the price mechanism, marginalism, or supply and demand can go wrong.
[No one seems to be arguing that Hayek had a complete understanding of the price mechanism. However I will discuss the part about how markets "go wrong" in more detail below.]
4. But [Hayek] didn’t have the conceptual or mathematical tools of information theory to understand the mechanisms of that relationship
[This isn't even debatable. Hayek never used information theory to understand the price mechanism.]
There is also another thread about how I am supposedly claiming to be designing a machine learning algorithm that will work better than markets. However, this is just a reading comprehension failure as I claim the exact opposite:
The thing is that with the wrong settings, [machine learning] algorithms fail and you get garbage. I know this from experience in my regular job researching ... algorithms. Therefore depending on the input data (especially data resulting from human behavior), we shouldn’t expect to get good results all of the time. These failures are exactly the failure of information to flow from the real data to the generator through the detector – the failure of information from the demand to reach the supply via the price mechanism.
I was actually making an analogy that the failure of machine learning algorithms might be similar to the failure of markets. I do claim "The understanding of prices and supply and demand provided by information theory and machine learning algorithms is better equipped to explain markets", but again that doesn't mean machine learning is better than markets but rather a potential model of markets.

Now on to the more substantive complaints above ...

One of the main things Hayek got wrong was his "metaphor" (that he says is "more than a metaphor") of price as a communication system, from "The Use of Knowledge in Society" (1945):
We must look at the price system as such a mechanism for communicating information if we want to understand its real function—a function which, of course, it fulfills less perfectly as prices grow more rigid. (Even when quoted prices have become quite rigid, however, the forces which would operate through changes in price still operate to a considerable extent through changes in the other terms of the contract.) The most significant fact about this system is the economy of knowledge with which it operates, or how little the individual participants need to know in order to be able to take the right action. In abbreviated form, by a kind of symbol, only the most essential information is passed on and passed on only to those concerned. It is more than a metaphor to describe the price system as a kind of machinery for registering change, or a system of telecommunications which enables individual producers to watch merely the movement of a few pointers, as an engineer might watch the hands of a few dials, in order to adjust their activities to changes of which they may never know more than is reflected in the price movement.
The main point in my Evonomics article is that information is not passed through prices, and the markets are not transmitting information as a telecommunications system. His words are fairly straightforward. Hayek makes these claims about the price mechanism (emphasis mine in the quote above) despite the fact that they are inconsistent with information theory.

A more subtle and interesting point raised by Hayek fans was that Hayek never claimed the system was perfect or free from error or failures (that markets never "go wrong"). Again, from "The Use of Knowledge in Society":
Of course, these [price] adjustments are probably never "perfect" in the sense in which the economist conceives of them in his equilibrium analysis. But I fear that our theoretical habits of approaching the problem with the assumption of more or less perfect knowledge on the part of almost everyone has made us somewhat blind to the true function of the price mechanism and led us to apply rather misleading standards in judging its efficiency. The marvel is that in a case like that of a scarcity of one raw material, without an order being issued, without more than perhaps a handful of people knowing the cause, tens of thousands of people whose identity could not be ascertained by months of investigation, are made to use the material or its products more sparingly; i.e., they move in the right direction. This is enough of a marvel even if, in a constantly changing world, not all will hit it off so perfectly that their profit rates will always be maintained at the same constant or "normal" level.
I was well aware that Hayek did say the price mechanism could fail (famously in the case of government interference such as taxes or subsidies). However my claim was that Hayek doesn't tell you "the ways the price mechanism ... can go wrong" -- not that he doesn't tell you "that the price mechanism ... can go wrong". In my description of non-ideal information transfer, I show mathematically that market failures lead to lower prices. That's a way markets fail. Although I didn't go into it in the article, correlations among agents are one way to get non-ideal information transfer (essentially a failure of the maximum entropy assumptions). Markets also can fail if you don't have enough transactions. Where Hayek says airplanes can crash, I claim Hayek doesn't tell us how airplanes crash but information theory does.

Strictly speaking, this is not entirely true. Hayek does claim that price controls and other government interventions will cause the price mechanism to fail. However the failure mode I talk about in my article does not require government intervention, and the implication when I say that "the price mechanism, marginalism, or supply and demand can go wrong" is that we are talking about the possibility of failure even in the case free markets. Hayek also says that the market is self-correcting (emphasis in the previous quote), but this is only true in the case of nearly ideal information transfer.

*  *  *

As I don't make that many claims about Hayek, the corpus of material required to understand those claims about Hayek is actually relatively small. It's also not hard to understand what Hayek was saying in general about the price mechanism: prices are a way to get information about a drought in one region to markets in another. But while his intuition was useful, you have to be consistent with information theory which leads to a better understanding of the possible failure modes.

I wasn't talking about the "economic calculation problem" (about central planning), the business cycle, or any of the politics in e.g. The Road to Serfdom so references to those topics aren't germane to the discussion of Hayek and the price mechanism. Therefore a lot of the criticism of my Evonomics article misses the point.

PS For those interested, I have a more detailed argument about how markets can fail to aggregate information (they represent a heuristic algorithm solution to the allocation problem, but not the information aggregation problem). 

PPS I have an animation I started to put together about this subject several years ago that was never completed:

The animation first describes Hayek's information aggregation function (where the all-knowing market spits out a price after aggregating all the information. The second part shows the information equilibrium picture where the price is just "listening in" (using a particularly 2013-relevant metaphor).

Friday, May 19, 2017

Principal component analysis of state unemployment rates

One of my hobbies on this blog is to apply various principal component analyses (PCA) to economic data. For example, here's some jobs data by industry (more here). I am not saying this is original research (many economics papers have used PCA, but a quick Googling did not turn up this particular version).

Anyway, this is based on seasonally adjusted FRED data (e.g. here for WA) and I put the code up in the dynamic equilibrium repository. Here is all of the data along with the US unemployment rate (gray):

It's a basic Karhunen–Loève decomposition (Mathematica function here). Blue is the principal component (first principal component), and the rest of the components aren't as relevant. To a pretty good approximation, the business cycle in employment is a national phenomenon:

There's an overall normalization factor based on the fact that we have 50 states. We can see the first (blue) and second (yellow) components alongside the national unemployment rate (gray, right scale): 

Basically the principal component is the national business cycle. The second component is interesting as it suggests differences in different states based on the two big recessions of the past 40 years (1980s and the Great Recession) that go in opposite directions. The best description of this component is that it represents that some states did much worse in the 1980s and some states did a bit better in the 2000s (see the first graph of this post).

As happened before, the principal component is pretty well modeled by the dynamic equilibrium model (just like the national data):

The transitions (recession centers) are at 1981.0, 1991.0, 2001.7, 2008.8 and a positive shock at 2014.2. These are consistent with the national data transitions (1981.1, 1991.1, 2001.7, 2008.8 and 2014.4).

Wednesday, May 17, 2017

My article at Evonomics

I have an article up at Evonomics about the basics of information equilibrium looking at it from the perspective of Hayek's price mechanism and the potential for market failure. Consider this post a forum for discussion or critiques. I earlier put up a post with further reading and some slides linked here.

I also made up a couple of diagrams that I didn't end up using illustrating price changes:

Tuesday, May 16, 2017

Explore more about information equilibrium

Originally formulated by physicists Peter Fielitz and Guenter Borchardt for natural complex systems, information equilibrium [arXiv:physics.gen-ph] is a potentially useful framework for understanding many economic phenomena. Here are some additional resources:

A tour of information equilibrium
Slide presentation (51 slides)

Dynamic equilibrium and information equilibrium
Slide presentation (19 slides)

Maximum entropy and information theory approaches to economics
Slide presentation (27 slides)

Information equilibrium as an economic principle
Pre-print/working paper (44 pages)

Saturday, May 13, 2017

Theory and evidence in science versus economics

Noah Smith has a fine post on theory and evidence in economics so I suggest you read it. It is very true that there should be a combined approach:
In other words, econ seems too focused on "theory vs. evidence" instead of using the two in conjunction. And when they do get used in conjunction, it's often in a tacked-on, pro-forma sort of way, without a real meaningful interplay between the two. ... I see very few economists explicitly calling for the kind of "combined approach" to modeling that exists in other sciences - i.e., using evidence to continuously restrict the set of usable models.

This does assume the same definition of theory in economics and science, though. However there is a massive difference between "theory" in economics and "theory" in sciences. 

"Theory" in science

In science, "theory" generally speaking is the amalgamation of successful descriptions of empirical regularities in nature concisely packaged into a set of general principles that is sometimes called a framework. Theory for biology tends to stem from the theory of evolution which was empirically successful at explaining a large amount of the variation in species that had been documented by many people for decades. There is also the cell model. In geology you have plate tectonics that captures a lot of empirical evidence about earthquakes and volcanoes. Plate tectonics explains some of the fossil record as well (South America and Africa have some of the same fossils up to a point at which point they diverge because the continents split apart). In medicine, you have the germ theory of disease.

The quantum field theory framework is the most numerically precise amalgamation of empirical successes known to exist. But physics has been working with this kind of theory since the 1600s when Newton first came up with a concise set of principles that captured nearly all of the astronomical data about planets that had been recorded up to that point (along with Galileo's work on projectile motion).

But it is important to understand that the general usage of the word "theory" in the sciences is just shorthand for being consistent with past empirical successes. That's why string theory can be theory: it appears to be consistent with general relativity and quantum field theory and therefore can function as a kind of shorthand for the empirical successes of those theories ... at least in certain limits. This is not to say your new theoretical model will automatically be correct, but at least it doesn't obviously contradict Einstein's E = mc² or Newton's F = ma in the respective limits.

Theoretical biology (say, determining the effect of a change in habitat on a species) or theoretical geology (say, computing how the Earth's magnetic field changes) is similarly based on the empirical successes of biology and geology. These theories are then used to understand data and evidence and can be rejected if evidence contradicting them arises.

As an aside, experimental sciences (physics) have an advantage over observational ones (astronomy) in that the former can conduct experiments in order to extract the empirical regularities used to build theoretical frameworks. But even in experimental sciences, experiments might be harder to do in some fields than others. Everyone seems to consider physics the epitome of science, but in reality the only reason physics probably had a leg up in developing the first real scientific framework is that the necessary experiments required to observe the empirical regularities are incredibly easy to set up: a pendulum, some rocks, and some rolling balls and you're pretty much ready to experimentally confirm everything necessary to posit Newton's laws. In order to confirm the theory of evolution, you needed to collect species from around the world, breed some pigeons, and look at fossil evidence. That's a bit more of a chore than rolling a ball down a ramp.

"Theory" in economics

Theory in economics primarily appears to be solving utility maximization problems, but unlike science there does not appear to be any empirical regularity that is motivating that framework. Instead there are a couple of stylized facts that can be represented with the framework: marginalism and demand curves. However these stylized facts can also be represented with ... supply and demand curves. The question becomes what empirical regularity is described by utility maximization problems but not by supply and demand curves. Even the empirical work of Vernon Smith and John List can be described by supply and demand curves (in fact, at the link they can also be described by information equilibrium relationships).

Now there is nothing wrong with using utility maximization as a proposed framework. That is to say there's nothing wrong with positing any bit of mathematics as a potential framework for understanding and organizing empirical data. I've done as much with information equilibrium.

However the utility maximization "theory" in economics is not the same as "theory" in science. It isn't a shorthand for a bunch of empirical regularities that have been successfully described. It's just a proposed framework; it's mathematical philosophy.

The method of nascent science

This isn't necessarily bad, but it does mean that the interplay between theory and evidence reinforcing or refuting each other isn't the iterative process we need to be thinking about. I think a good analogy is an iterative algorithm. This algorithm produces a result that causes it to change some parameters or initial guess that is fed back into the same algorithm. This can converge to a final result if you start off close to it, but it requires your initial guess to be good. This is the case of science: the current state of knowledge is probably decent enough that the iterative process of theory and evidence will converge. You can think of this as the scientific method ... for established science.

For economics, it does not appear that the utility maximization framework is close enough to the "true theory" of economics for the method of established science to converge. What's needed is the scientific method that was used back when science first got its start. In a post from about a year ago, I called this the method of nascent science. That method was based around the different metric of usefulness rather than model rejection in established science. Here's a quote from that post:
Awhile ago, Noah Smith brought up the issue in economics that there are millions of theories and no way to reject them scientifically. And that's true! But I'm fairly sure we can reject most of them for being useless.

"Useless" is a much less rigorous and much broader category than "rejected". It also isn't necessarily a property of a single model on its own. If two independently useful models are completely different but are both consistent with the empirical data, then both models are useless. Because both models exist, they are useless. If one didn't [exist], the other would be useful.
Noah Smith (in the post linked at the beginning of this post) put forward three scenarios of theory and evidence in economics:
1. Some papers make structural models, observe that these models can fit (or sort-of fit) a couple of stylized facts, and call it a day. Economists who like these theories (based on intuition, plausibility, or the fact that their dissertation adviser made the model) then use them for policy predictions forever after, without ever checking them rigorously against empirical evidence. 
2. Other papers do purely empirical work, using simple linear models. Economists then use these linear models to make policy predictions ("Minimum wages don't have significant disemployment effects"). 
3. A third group of papers do empirical work, observe the results, and then make one structural model per paper to "explain" the empirical result they just found. These models are generally never used or seen again.
Using these categories, we can immediately say 1 & 3 are useless. If a model never checked rigorously against data or if a model is never seen again, they can't possibly be useful.

In this case, the theories represent at best mathematical philosophy (as I mentioned at the end of the previous section). It's not really theory in the (established) scientific sense.


Mathematical Principles of Natural Philosophy

Sometimes a little bit of mathematical philosophy will have legs. Isaac Newton's work, when it was proposed, was mathematical philosophy. It says so right in the title. So there's nothing wrong with the proliferation of "theory" (by which we mean mathematical philosophy) in economics. But it shouldn't be treated as "theory" in the same sense of science. Most if it will turn out to be useless, which is fine if you don't take it seriously in the first place. And using economic "theory" for policy would be like using Descartes to build a mag-lev train ...


Update 15 May 2017: Nascent versus "soft" science

I made a couple of grammatical corrections and added a "does" and a "though" to the sentence after the first Noah Smith quote in my post above.

But I did also want to add the point that by "established science" vs "nascent science" I don't mean the same thing as many people mean when they say "hard science" vs "soft science". So-called "soft" sciences can be established or nascent. I think of economics as a nascent science (economies and many of the questions about them barely existed until modern nation states came into being). I also think that some portions will eventually become a "hard" science (e.g. questions about the dynamics of the unemployment rate), while others might become a "soft" science with the soft science pieces being consumed by sociology (e.g. questions about what makes a group of people panic or behave as they do in a financial crisis).

I wrote up a post that goes into that in more detail about a year ago. However, the main idea is that economics might be explicable -- as a hard science even -- in cases where the law of large numbers kicks in and agents do not highly correlate (where economics becomes more about the state space itself than the actions of agents in that state space ... Lee Smolin called this "statistical economics" in an analogy with statistical mechanics). 

I think for example psychology is an established soft science. Its theoretical underpinnings are in medicine and neuroscience. That's what makes the replication crisis in psychology a pretty big problem for the field. In economics, it's actually less of a problem (the real problem is not the replication issue, but that we should all be taking the econ studies less seriously than we take psychology studies).

Exobiology or exogeology could be considered nascent hard sciences. Another nascent hard science might be so-called "data science": we don't quite know how to deal with the huge amounts of data that are only recently available to us and the traditional ways we treat data in science may not be optimal.

Monday, May 8, 2017

Government spending and receipts: a dynamic equilibrium?

I was messing around with FRED data and noticed that the ratio of government expenditures to government receipts seems to show a dynamic equilibrium that matches up with the unemployment rate. Note this is government spending and income at all levels (federal + state + local). So I ran it through the model [1] and sure enough it works out:

Basically, the ratio of expenditures to receipts goes up during a recession (i.e. deficits increase at a faster rate) and down in the dynamic equilibrium outside of recessions (i.e. deficits increase at a slower rate or even fall). The dates of the shocks to this dynamic equilibrium match pretty closely with the dates for the shocks to unemployment (arrows).

This isn't saying anything ground-breaking: recessions lower receipts and increase use of social services (so expenditures over receipts will go up). It is interesting however that the (relative) rate of improvement towards budget balance is fairly constant from the 1960s to the present date ... independent of major fiscal policy changes. You might think that all the disparate changes in state and local spending is washing out the big federal spending changes, but in fact the federal component is the larger component so it is dominating the graph above. In fact, the data looks almost the same with the just the federal component (see result below). So we can strengthen the conclusion: the (relative) rate of improvement towards federal budget balance is fairly constant from the 1960s to the present date ... independent of major federal fiscal policy changes.



[1] The underlying information equilibrium model is GE ⇄ GR (expenditures are in information equilibrium with receipts, except during shocks).

Friday, May 5, 2017

Dynamic equilibrium in employment-population ratio in OECD countries

John Handley asks on Twitter about whether the dynamic equilibrium model works for the unemployment-population ratio for other countries besides the US. So I re-ran the model on some of the shorter OECD time series available on FRED (most of them were short, and I could easily automate the procedure for time series of approximately the same length).

As with the US, some countries seem to be undergoing a "demographic transition" with women entering the workforce. Therefore most of the data sets are for men only. I just realized that I actually have both for Greece. These are all for 15-64 year olds, and cases where there was data for at least 2000-2017. Some of the series only go back to 2004 or 2005, which is really too short to be conclusive. I also left off the longer time series (to come later in an update) because it was easy to automate the model for time series of approximately the same length.

Anyway, the men-only model country list is: Denmark, Estonia, Greece, Ireland, Iceland, Italy, New Zealand, Portugal, Slovenia, Turkey, and South Africa. The men and women are included for: France, Greece (again), Poland, and Sweden. I searched FRED manually, so these are just the countries that came up.

Here are the results (some have 1 shock, some have 2):

What is interesting is that while the global financial crisis seems to often be conflated with the Greek debt crisis, the Greek debt crisis appears to hit much later (centered at 2011.2). For example, the recession in Iceland is centered at 2008.7 (about 2.5 years earlier, closer to the recession center for the US).



Here are the results for Australia, Canada, and Japan which have longer time series:

"You're wrong because I define it differently"

There is a problem in the econoblogosphere, especially among heterodox approaches, where practitioners do not recognize that their approach is non-standard. I'm not trying to single out commenter Peiya, but this comment thread is a teachable moment, and I thought my response had more general application. 

Peiya started off saying:
Many economic theories are based on wrong interpretation on accounting identities and underlying data semantics.
and went on to talk about a term called "NonG". In a response to my question about the definitions of "NonG", Peiya responded:
Traditional definition of the "income accounting identity" (C+I+G = C + S + T or S-I = G-T) is widely-misused with implicit assumption NonG = 0.
So Peiya was using a different definition. My response is what I wanted to promote to a blog post (with one change to link to Paul Romer's blog post on Feynman integrity where I realize the direct quote uses the word "leaning" rather than "bending"):
For the purposes of this blog, we'll stick to the traditional definition unless there is e.g. a model of empirical data that warrants a change of definition. Changing definitions of accounting identities and saying "Many economic theories are based on wrong interpretation on accounting identities" is a bit disingenuous. 
Imagine if I said you were wrong because I define accounting identities as statistical equilibrium potentials? I could say that there is no entropic force associated with your "nonG" term, therefore you have a wrong interpretation of the accounting identities. 
But I don't say that. And you shouldn't say that about the "traditional" definition of accounting identities unless you have a really good reason backed up with some peer-reviewed research or at least open presentations of that research. 
You must always try to "[bend] over backwards" to consider the fact that you might be wrong. Or at least note when you are considering some definition that is non-standard that it is in fact non-standard. In my link above, I admit the approach is speculative. I say "At least if [the equation presented] is a valid way to build an economy." I recognize that it is a non-standard definition of the accounting identities. 
Saying people misunderstand a definition and then presenting a non-standard version of that definition is not maintaining the necessary integrity for intellectual discussion and progress.
I've encountered this many times where people basically assume their own approach is a kind of null hypothesis and other people are wrong because they didn't use their definitions of their model. Even economists with Phds sometimes do this. However "You're wrong because I define it differently" is not a valid argument, and it's even worse if you just say "You're wrong" leaving off the part about the definition because you assume everyone is using your definition for some reason. The only people who can assume other people are using their definition are mainstream economists because that's the only way science and academia operates. The mainstream consensus is the default, and not recognizing the mainstream consensus or mainstream definitions is failing to lean over backwards and show Feynman integrity

Commenter maiko followed up with something that is also a teachable moment:
maybe by nature he is just harsher on confused post keynesians and more compliant with asylum inmates.
By "he" maiko is referring to me, and by "asylum inmates", maiko is referring to mainstream economists (at least I think so).

And yes, that's exactly right. At least when it comes to definitions. There are thousands of books and thousands of education programs in the world teaching the mainstream approach to economics. Therefore mainstream economic definitions are the default. If you want to deviate from them, that's fine. However, because the mainstream definitions are the default you need to 1) say you are deviating from them, and 2) have a really good reason for doing so (preferably because it allows you to explain some empirical data).


In my Tweet of this post, I said that in order to have academic integrity, you must recognize the academic consensus. This has applications far beyond the econoblogosphere and basically sums up the problem with Charles Murray (failing to have academic integrity because he fails to recognize that the academic consensus is that his research is flawed) as well as Bret Stephens in the New York Times (in a twitter argument) who not only failed to recognize the scientific consensus but actually put false statements in his OpEd.

Thursday, May 4, 2017

Labor force dynamic equilibrium

Employment data comes out tomorrow and I have some forecasts that will be "marked to market" (here's the previous update). If the unemployment rate continues to fall, then we're probably not seeing the leading edge of a recession.

I thought I'd add a look at the civilian labor force with the dynamic equilibrium model:

In this picture, we have just two major events over the last ~70 years in the macroeconomy: women entering the workforce and the Great Recession (where people left the workforce). This is the same general picture for inflation and output (see also here). Everything else is a fluctuation.

We'll get a new data point for this series tomorrow as well, so here's a zoomed-in version of the most recent data:


Update 5 May 2017

Here's that unemployment rate number. It's looking like the no-recession conditional forecast is the better one: