It’s been nearly a year since the stimulus package of 2009 was passed. Unfortunately most attempts to answer the question “What was the size of the impact?” are still based on economic models in which the answer is built-in, and was built-in well before the stimulus. Frequently the same economic models that said, a year ago, the impact would be large are now trotted out to show that the impact is large. In other words these assessments are not based on the actual experience with the stimulus. I think this has confused public discourse.
An example is a November 21 news story in the New York Times with the headline “New Consensus Sees Stimulus Package as a Worthy Step.” Authors Jackie Calmes and Michael Cooper write that “the accumulation of hard data and real-life experience has allowed more dispassionate analysts to reach a consensus that the stimulus package, messy as it is, is working. The legislation, a variety of economists say, is helping an economy in free fall a year ago to grow again and shed fewer jobs than it otherwise would.”
As evidence the article includes three graphs, which are reproduced on the left of the chart below. Each of the three graphs on the left corresponds to a Keynesian model maintined by the group shown above the graph. All three graphs show that without the stimulus the recovery would be considerably weaker. The difference between the black line and the gray line is their estimated impact of the stimulus. But this difference was built-in to these models before the stimulus saw the light of day. So there are no new hard data or real life experiences here.
Menzie Chen has a post on Econbrowser which mentioned the three graphs in the original New York Times article as an illustration of his excellent analysis of the use of counterfactuals (the gray lines in the graphs). The additional two graphs illustrate how important it is to go beyond a few models and establish robustness in policy analysis. Moreover, in my view, the models have had their say. It is now time to look at the direct impacts using hard data and real life experiences.