How Sticky Wages and a Flock of Ducks Can Guide Economic Policy

How Sticky Wages and a Flock of Ducks Can Guide Economic Policy


Modern macroeconomic models did not perform well during the Great Recession. What needs to be done to fix them? Can the existing models be patched up, or are brand new models needed?

Modern models came into existence during the methodological revolution in the 1970s. Prior to that time, macroeconomic models focused on the behavior of macroeconomic aggregates such as GDP, consumption, and investment. A typical equation in these models would assume that aggregate consumption – the sum total of consumption of individual households – depends upon aggregate income. However, there is no guarantee that these equations are consistent with utility maximizing of households or profit maximizing behavior of firms. 

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For example, the older models often assumed that wages were sticky, and this meant that there would be an excess supply of labor – it would result in unemployment. That gives us a way to explain unemployment, but we know that when this is the case, i.e. when we are not at the point where supply equals demand, we can make everyone better off by moving to the equilibrium point. So why would firms or workers negotiate such an outcome? Why would both sides, in essence, leave money on the table?

There are ways to make sticky wages consistent with optimizing behavior, but it requires us to begin modeling at the level of the individual firm or household. This is what the methodological revolution of the 1970s was all about. The idea was to start at the level of an individual firm or household, impose rational maximizing behavior, determine the equations describing their behavior, and then aggregate across household and firms to determine macroeconomic relationships (i.e. the aggregate labor supply curve was the sum of all the individual labor supply curves).

This “micro-foundations” approach solved the problem of having aggregate equations that were inconsistent with rational maximizing behavior, but it brought problems of its own. One of these was the difficulty of aggregating from households and firms to obtain macroeconomic relationships. It turns out that attempting to add up individual equations to obtain aggregate, macroeconomic relationships creates all sorts of difficulties that are difficult or impossible to overcome. 

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There is a way around this problem, the “representative agent” approach. The trick is to model the behavior of a single household and single firm, both of which are average or representative, and then assume the equations for these typical households and firms capture, on average, the behavior of all households and firms. 

Unfortunately, the representative agent approach is unsuited for studying behavior in financial markets. The problem is that there is no way for a single representative household to trade stocks and bonds with itself based upon different forecasts of future economic conditions (e.g. a person who thinks the price of a stock will fall in the future sells the stock to someone who believes the price will rise).

Until the financial crisis macroeconomists weren’t too concerned about this since they believed modern financial markets were insulated from the types of financial disturbances that caused the Great Depression, so there was no need to include financial markets in these models. It would simply complicate things immensely while adding very little to macroeconomic models.

The Great Recession made it clear this was a mistake – when the financial crisis hit modern models were not up to the task – and one of the main agendas in the post-crash period has been to overcome the aggregation issues so that the financial sector can be included in modern models. There has been some progress on this front, but there is still a long way to go and it remains unclear whether existing models can be amended to overcome this problem. If not, then it will be time to drop this framework and search for alternatives that are better suited to the task. 

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But what form should these new models take? Should we retain the micro-foundations approach, or return to modeling aggregate relationships as was done prior to the methodological revolution of the 1970s? The older, aggregate style models seem to do a much better job of predicting macroeconomic outcomes – as I explained in my last column, during the crisis they behaved admirably – but it is never clear if the equations are consistent with rational maximizing behavior. The newer, micro-foundations approach avoids this issue, but it is extremely difficult to include financial markets – or anything else that involves interactions among agents in the model.

An analogy might help. To understand the behavior of a flock of ducks flying in formation, it is not enough to model the behavior of a single, representative duck. The modeler must also understand the rules ducks use to maintain their positions in flight – each duck reacts to those in its immediate surroundings – and the rules used to change which duck is in the point position. Capturing these “interactions among agents” is an extremely difficult modeling task in general, but if all the researcher is interested in is the aggregate behavior of the ducks, e.g. where they are headed and how long it will take to get there, then it’s sufficient to simply model the factors that influence their aggregate speed and direction.

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For predictions of this type, the complications that arise from the micro-foundations approach can be avoided. But for other questions, e.g. how ducks manage to stay in formation and distribute the load of flying point across the flock, or how the rules might be lead them off course, “micro-foundations” are needed. 

Thus, the approach to take depends critically on the question the researcher is asking. For some questions, the aggregate approach is best despite the criticism it has received in recent years from those using modern models, and we shouldn’t think of it as going backwards if we adopt this approach when it provides simple, fast, and accurate answers to our questions. The “correct” model to use is not an either/or decision, and macroeconomists should be open to both approaches as we try to improve our ability to understand the macro-economy, and provide policy advice when the economy experiences problems.

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