Next, I examine whether the choice of elasticities has implications for individual historical episodes. Figure 4 presents historical decompositions for two choices of εM. In both panels, εD = εQ = 1. In panel A, I set εM = 1; and, in panel B, εM = 0.1. With relatively high elasticities of substitution across inputs, each and every recession between 1960 and the present day is explained almost exclusively by the common shocks. The sole partial exception is the relatively mild 2001 recession. In 2001 and 2002, Non-Electrical Machinery, Instruments, F.I.R.E. (Finance, Insurance, and Real Estate), and Electric/Gas Utilities—together accounting for GDP growth rates that were 2.0 percentage points below trend.
Table 3, along with panel B of Figure 4, presents historical decompositions, now allowing for complementarities across intermediate inputs. Here, industry-specific shocks are a primary driver, accounting for a larger fraction of most, but certainly not all of, recent recessions and booms. According to the model-inferred productivity shocks, the 1974–1975 and, especially, the early 1980s recessions were driven to a large extent by common shocks.27 At the same time, the late 1990s expansion and the 2008–2009 recession are each more closely linked with industry-specific events. Instruments (essentially computer and electronic products) and F.I.R.E. had an outsize role in the 1996–2000 expansion, while wholesale/retail, construction, motor vehicles, and F.I.R.E. appear to have had a large role in the most recent recession.
Let’s think about this using an analogy. Suppose you study the causes of cycles in house collapses. Assume a community where 90% of houses have solid foundations, and 10% have rotten wood foundations. Also assume that during floods the rate of house collapses rises from 7 per week to 450 per week. A cross sectional study shows that 425 of the 450 collapsed houses during a flood had rotten foundations, while 25 had solid foundations. This despite the fact that only 10% of overall homes had rotten foundations.
How much of the “cycle” in house collapses is “caused” by floods, and how much is caused by rotten foundations? Show work.
The important question is: “How big would the business cycle be in a counterfactual where the Fed successfully stabilized NGDP growth?” I say “fairly small”.
Another question that is actually much less important, but seems more important to most people is: “How much of the instability in NGDP is due to monetary policy mistakes triggered by sectoral shocks, such as a decline in the natural rate of interest that the Fed overlooked, which was itself caused by a housing slump?”
When you read impressive looking empirical studies in top journals, do not assume that the authors are asking the right question.