The Difficulty with Economics TextbooksThe normal starting point for learning about a field is to pick up an undergraduate textbook. Within economics, this strategy generally does not work.
In order to be comprehensible to undergraduates, a textbook consists mainly of two types of material.
- Rote learning. In economics, this consists of topics such as the definition of GDP, what are price indices, why unemployment is defined the way it is, the definition of various monetary aggregate, etc. (The definitions of various econometric tools fits in here as well.)
- Simple models that undergraduates can reproduce on an exam, which are supposed to illustrate various principles.
If we look at mainstream undergraduates textbooks (and most of them are mainstream), the rote learning aspect is entirely reasonable, whereas the models are an unmitigated disaster. As a result, I believe that people interested in economics should pick up an undergraduate textbook, read the definitional parts, and not read any text that discusses economic behaviour.
|Money Flows in Model SIM|
There is at least one exception that I am aware of -- Godley and Lavoie's Monetary Economics. The focus on the book is on the models, but it is tied in with the background material (if you read the text, and not just jump to the mathematical bits). The difficulty with the methodology used therein is that the equations are derived by hand, and there are lots of equations. It is difficult to play around with the models, and see how they behave.
Model SIM with Profits
I discussed building model SIM (from Chapter 3 of Monetary Economics) using Python in a recent article. (For those of you who do not have Monetary Economics, the linked article references other articles which describe the model, and alternative ways of finding the model solution.)
One of the peculiarities of SIM is that business profits are assumed to be zero, and that property helps eliminate business sector behaviour from the model. This has to be done to make the model the simplest possible, as otherwise business sector behaviour adds new dynamics.
Observant readers of the code of my Python model will have noted the following lines:
# A literally non-profit business sector
bus = FixedMarginBusiness(can, 'Business Sector', 'BUS', profit_margin=0.0)What happens if we set profit_margin to be something other than 0? (For those of you who wonder I why I specify 0.0 instead of 0, the answer is not false precision, rather that I want Python to interpret the number as floating point, and not an integer.)
I built a model with two countries, that I labelled "Canada" and the "United States." The hapless Canadian business sector matches the original model SIM with zero profits, and the red-blooded capitalists in the United States have a fixed profit margin of 10%. (Although it is a two-country model, there are no cross-border linkages added; I just put them into a single model so that I can compare the outputs easily. The number of equations explodes, of course.)
The results above show total output (GDP). What we see is that for this simple model, profits are bad for output.Both countries converge to a steady state value, but the steady state is lower in the "United States".
Hoarding Business Sector
The chart above explains the root of the problem. The business sector in the United States is assumed to always have positive profits, and it uses those profits to increasingly hoard financial assets.
The Lesson?It would be quite easy to imagine the thinking of the hypothetical people living inside the model. One would imagine business leaders in the United States wringing their hands about persistent government budget deficits (and exploding debt-to-GDP ratio), but that is entirely the result of their hoarding behaviour. If we lump pension funds and mercantilist central banks within the "business sector," there is an analogy to what is happening in the real world.
However, these analogies are stretched. The problem is that this model has removed capitalists from capitalism. We need to see what happens when profits are recirculated via dividends. If we do, we get more sensible-looking steady state behaviour. My modelling framework supports that step, and it will probably be the subject of a follow up article (which will be delayed, as I have a backlog of rants to cover).
Digression: Whither Economics?Some recent articles have underlined my view that some of the outside critics of economics do not understand what is really going on. (Since I have no training in economics, I am also an outsider, but I am familiar with the issues involved as a result of my old day jobs.)
The obvious weaknesses of mainstream models creates a tendency for outsiders to completely discount the contents of undergraduate economics textbooks. That is, throwing the baby out with the bath water. We then end up with people attempting to completely reinvent economics, using insights from whatever field they come from. (Physicists are a clear leader in this category, but us engineers are in second place.)
These outsiders with visions of reinventing macro ignore one point: there is not a whole lot of debate about the "rote learning" parts of textbooks. Yes, there are debates about how classify transactions within the national accounts (and how they should be constructed), but no serious economist debates how the national accounts are currently constructed.
The disputes within economics revolve around:
- How do economic entities behave?
- How should economies be organised ("normative" debates)?
I seriously doubt that a physicist is going to settle the latter question (normative debates) any time soon. As for the question of economic behaviour ("positive questions"), all I can say is: good luck. Take the current situation in Canada. The housing market is over-extended in a fashion very similar to that in the United States in 2007. Any economic forecast has to be conditional upon the outlook for housing. However, the odds of creating a mathematical model that can correctly forecast inflation, unemployment, and the housing market simultaneously are slim.
The best we can hope for is to have a framework to position our thinking about the economy, in particular the effect of policy changes. Stock-Flow Consistent models provide such a framework. My hope is that my Python module will make it easier for people to work through the basics of how the economic sectors fit together, and understand how the behavioural debates affect observed economic behaviour. Actually playing with the models is superior to reading stories about them, as the story telling needs to be validated.
Appendix: Python Code
(c) Brian Romanchuk 2016