Having discussed the purpose of economic models in the first post in this series, in the previous post, I looked at the first of three exemplary assumptions employed by (macro-) economists — that households are infinitely lived. My goal is to show why, despite appearing to be outlandish, such assumptions are used and, indeed, can be useful.
A second assumption that has come under heavy scrutiny is to presume that agents have rational expectations. Intuitively, that amounts to saying that agents “know the model” and form expectations about the future that are correct on average over time, i.e. their expectations aren’t systematically biased. One justification for this assumption — surprise — is that it makes the models easier to solve. A second justification is that certain alternatives have very uncomfortable implications. For instance, imagine we’d assume instead that your expectation for next year’s level of inflation (i.e. how much prices go up) is always the value inflation has today — so-called ‘purely adaptive expectations’. Now suppose that the central bank in your country tells you it’s going to do everything it take to raise inflation next year; in fact, everything around you points towards a rapid rise in inflation [1 ]. If we assume adaptive expectations, we basically have to suppose that agents completely disregard all these signals — and that’s not a very good assumption either! Fortunately there’s a large number of alternatives to strict rational expectations that have and are being studied by economists. We might assume that some people, or people sometimes, have rational expectations but at other times are ‘backward-looking’ — in fact, many central banks employ models with a structure of this sort. Or we can explicitly model learning (here’s a good interview with one of the leading researchers on the topic) or introduce limitations as suggested by behavioural economics. Doing so typically comes at the cost of greater complexity — which makes the model difficult to solve — or introducing greater simplicity elsewhere. In all such cases, rational expectations represents a helpful benchmark with which we may compare these alternatives. Moreover, if we’re not really interested in questions of information-processing, then assuming rational expectations may be a neat shortcut that permits us to introduce greater complexity in other aspects of the economy.
Finally, and perhaps most bizarrely — at least judging on my own intuition and a highly unrepresentative survey among fellow grad students at Oxford — macroeconomists frequently assume a ‘representative agent‘. That is to say, we typically assume that there are loads and loads of agents in the economy, but that they’re all identical, e.g. in terms of what they like (‘preferences’), how wealthy they are or how smart they are. Put differently, we set aside heterogeneity. We also speak of a representative agent when economic actors do in fact differ, but in such a way that if we add up their choices, the result is mathematically equivalent to the choice taken by one individual (or many identical individuals). Thus, in 2003 perhaps the most influential living macroeconomist, Robert Lucas, pithily stated: “For determining the behavior of aggregates [Krusell and Smith (1998)] realistically modeled household heterogeneity just does not matter very much. For individual behavior and welfare, of course, heterogeneity is everything.” If that sounds confusing, imagine you and your nine friends together buy ten apples regardless of how we distribute money amongst you, then we may as well suppose that you’re one agent as long as we’re concerned only with the aggregate consumption of apples. This example might illustrate why this assumption can in fact be justifiable, at least for particular research questions and particular situations. My personal view, though, is that for a large number of issues that economists ought to confront — note how my normative view on what questions are important comes in — the assumption of a representative agent is not useful. For one, I think in the roughly one and a half decade since Lucas made the above quoted statement, economists have realised that heterogeneity does matter for aggregate outcomes. To give two intuitive examples, people with a lot of debt may respond differently to a change in fiscal or monetary policy (the former is in the realm of e.g. the US Treasury whereas the latter is decided by the US Federal Reserve); and automation may affect someone with a college education differently than someone without comparably high “human capital”, as economists would call it. Secondly, I have doubts whether we can use aggregate outcomes, i.e. the level of analysis for which Lucas says realistically modeling heterogeneity does not matter much, In other words, I think inequality matters, and matters a lot, for whether some policy outcome is good or bad. Unfortunately, saying that it’d be nice to include heterogenous agents isn’t enough — doing so requires a lot of brain power and at least as much computational power; and the latter we didn’t have until recently. Fortunately, economists have and are developing “heterogenous agent models” — most people would probably say “non-crazy models” — where the individuals or households we model, or indeed firms and other actors, differ along various dimensions (here’s a good example and e.g. at the Uni of Bonn researchers are setting up a new group that is entirely devoted to the “Macroeconomics of Inequality”). In other words, even though I’ve argued that in some cases assuming “representative agents” can make sense, I also believe that we’re making progress towards being capable of analysing complex models in which we can include such things as me liking chocolate better than you .
In summary, in this post and the preceding one I’ve given three examples of assumptions that macroeconomists make when they build particular models (‘maps’ of the economy, in terms of Borges’ short story discussed in the first post) — assumptions that probably strike the reader as crazy . I’ve tried to provide a rationale for why such modelling decisions have been made and why they can be useful and helpful for particular purposes. I’ve flagged that economists have built models that feature alternative assumptions, but that doing so may come at a cost (e.g. in terms of greater complexity). Finally, in the last paragraph, I’ve argued that including heterogeneity instead of representative agents in macroeconomic models — an area I’m very interested in, admittedly — may be valuable in order to answer a number of important questions. My hope is that this simple discussion helped convey to non-economists #WhatEconomistsReallyDo.
 Implicitly, I’m assuming that you have some kind of economic model in your head. But in fact that’s not a particularly strong assumption to make. Suppose there are 100 apples in the economy and 100 people who own £1 each. Now give each person another £1 but change nothing else. I bet you’d predict that the price of apples will go up, wouldn’t you? Well, that’s because you have a simple economic model in your mind, one in which more money chasing the same amount of goods drives up prices.
 Another dimension of heterogeneity is on the firm side. For instance, economists try to better understand why companies like Facebook or Google or Volkswagen are very successful while others are much less so, and what this implies for the macro-economy.
 No doubt, there are many other such assumptions, e.g. that agents maximise utility (or profits), that the economy is stable unless it’s hit by shocks, that labour productivity is “innate and transferable”, whereas many organisations invest into their employees’ organisation-specific skills.