In macroeconomics, research almost always follows a similar pattern. First, the economist comes up with a question. Maybe they look at data and generate some stylized facts about some aspect of the economy. Then they set out to “explain” these facts using a structural economic model (I put explain in quotes because this step usually involves stripping away everything that made the question interesting in the first place). Using their model, they can then make some predictions or do some policy analysis. Finally, they write a paper describing their model and its implications.
There is nothing inherently wrong with this approach to research. But there are some issues. The first is that every paper looks exactly the same. Every paper needs a model. Sometimes papers adapt existing models, but they need enough difference to be a contribution on their own (but not so much difference that you leave the narrow consensus of modern macroeconomic methodology). Rarely, if ever, is there any attempt to compare models, to evaluate their failures and successes. It’s always: previous papers missed this and that feature while mine includes it.
This kind of iterative modeling can give the illusion of progress, but it really just represents sideways movement. The questions of macroeconomics haven’t really changed much in the last 100 years. What we have done is develop more and more answers to those questions without really making any progress on figuring out which of those answers is actually correct. Thousands of answers to a question is in many ways no better than none at all.
I don’t think it’s too much of a mystery why macroeconomics looks this way. Everybody already knows how an evaluation of our current answers to macroeconomic questions would go. The findings: we don’t know anything and all our models stink. I’d be surprised if even 10% of economists would honestly suggest a policymaker to carry out the policy that their papers suggest.
Academics still need to publish of course so they change the criteria that describes good macroeconomic research. Rarely is a paper evaluated on how well it answers an economic question. Instead, what matters is the tool used to answer the question. An empirical contribution without a model will get yawns in a macro seminar. A new mathematical contribution that uses a differential equations derived from a heat diffusion equation from physics? Mouths will be watering.
The claim is that these tools can then be used by other researchers as we continue to get closer and closer to the truth. The reality is that they are used by other researchers, but they only use the tools to develop their own slightly “better” tool in their own paper. In other words, the primary consumer of economic papers is economists who want to write papers. Widely cited papers are seen as better. Why? Because they helped a bunch of other people write their own papers? When does any of this research start to actually be helpful to people who aren’t responsible for creating it? Should we measure the quality of beef by how many cows it can feed?
Again there is an easy explanation for why economists are the only ones who can read economics papers. They would be completely unintelligible to anybody else. Reading and understanding the mechanism behind a macroeconomic paper is often a herculean task even for a trained economist. A non-expert has no chance. There could be good reasons for this complexity. I don’t expect to be able to open a journal on quantum mechanics and get anything out of it. But there is one enormous difference between physics and economics models. The physics ones actually work.
Economics didn’t always look this way. Read a paper by Milton Friedman or Armen Alchian. Almost no equations, much less the giant dynamic systems in models today. Does anybody think modern economic analysis is better than the kind done by those two?
The criticism of doing economics in words rather than math is that it is harder to be internally consistent. An equation has fewer interpretations than a sentence. I’m sympathetic to that argument. But I think there are better ways to add transparency to economics than by writing everything out in math that requires 20 years of school to understand. There are ways to formalize arguments other than systems of equations, ways to explain the mechanism that generates the data other than structural DSGE models.
The problem with purely verbal arguments is that you can easily lose your train of logic. Each sentence can make sense on its own but completely contradict another piece of the argument. Simultaneous systems of equations can prevent this kind of mistake. They are just one way. Computer simulations can provide the same discipline. Let’s say I have some theory about the way the world works. If I can design a computer simulation that replicates the kind of behavior I described in words, doesn’t that prove that my argument is logically consistent? It of course doesn’t mean I am right, but neither does a mathematical model. Each provides a complete framework that an outside observer can evaluate and decide whether its assumptions provide a useful view of the world.
The rest of the profession doesn’t seem to agree with me that a computer simulation and a system of equations serve the same purpose. I’m not exactly sure why. One potential worry is that it’s harder to figure out what’s actually happening in a computer simulation. With a system of equations I can see exactly which variables affect others and the precise channel of each kind of change. In a simulation, outcomes are emergent. Maybe I develop a simulation where an increase in taxes causes output to fall. Simply looking at the rules I have given each agent of how to act might not tell me why that fall occurred. It might be some complex interaction between these agents that generates that result.
That argument makes sense, but I think it only justifies keeping mathematical models rather than throwing out computer models. They serve different purposes. And computer models have their own advantages. One, which has yet to be explored in any serious way, is the potential for visual results. Imagine that the final result of an economic paper was not a long list of greek symbols and equals signs, but rather a full moving mini economic world. Agents move around, trade with each other. Firms set prices, open and close. Output and unemployment rise and fall. A simple version of such a model is the “sugarscape” model of Axtell and Epstein which creates a simple world where agents search for and trade sugar in order to survive.
Now imagine a much more complicated version of that that looked a lot more like a real economy. Rather than being able to “see” the relationships between variables in an equation, I could literally see how agents act and interact visually. My ideal world of economic research would not be writing papers, but creating apps. I want to download your model on my computer and play with it. Change the parameter values, apply different shocks, change the number and types of agents. And then observe what happens. Will this actually tell us anything useful about the economy? I’m not sure. But I think it’s worth a try. (I’m currently trying to do it myself. Hopefully I can post a version of it here soon)