Time to Plan and Aggregate Fluctuations

Studies of major capital projects report two facts about investment gestation: projects take longer than a quarter to complete, and they open with a lengthy planning phase – drawing plans, arranging finance, obtaining permits – during which the direct resource cost is small relative to the project total. Kydland and Prescott’s Time to Build built the first fact into a macro model; this paper argues the second is the one that matters quantitatively, and that the first “per se has relatively modest implications for business cycle dynamics.” The authors take Christiano and Eichenbaum’s divisible-labour model with technology and government consumption shocks and compare three investment technologies: one-period completion; Kydland and Prescott’s four-period gestation with resource weights of 0.25 in each quarter (time to build); and the same four-period gestation reweighted to 0.01, 0.33, 0.33, 0.33 so that the first quarter consumes almost nothing (time to plan). The empirical basis is explicit – in Mayer’s (1960) data projects took 22 months on average with the first 7 months a preconstruction planning phase, and Krainer (1968) finds that in all 25 projects he studies less than 5 percent of total cost was incurred in the first three months, and in 18 of them less than 2.5 percent. The mechanism the planning phase supplies is a delay in the response of hours worked. In a standard model a positive technology shock makes households work harder to accumulate the investable resources needed to exploit the higher return on investment; with a planning phase there is little to do with those resources in the period of the shock, so hours worked actually falls a little, investment barely moves, and much of the extra output is simply consumed. That delay in hours translates into a delay in output, and produces three matches to U.S. quarterly data for 1947:1-1995:1. First, persistence: the first-order autocorrelation of U.S. GDP growth is 0.37 (standard error 0.07), the one-period and four-period even-weight models produce essentially zero even though the exogenous technology growth rate is serially uncorrelated, and the time-to-plan model produces 0.36. Second, the timing of productivity and hours: because hours are damped on impact while productivity jumps, productivity comes to lead hours worked, and the contemporaneous hours-productivity correlation falls from roughly 0.90 in the other two models to 0.28 – the U.S. figure being near zero with a significantly positive correlation between productivity and future hours. Third, investment now lags output, which is counterfactual for aggregate investment but matches the behaviour of business investment in structures and equipment, the components for which a planning period is most plausible. The authors are careful about what does not work: consumption leads the cycle and is far too volatile in the time-to-plan model, both counterfactual, and they attribute this to the level of aggregation rather than to the mechanism, conjecturing that a model separating business structures from residential investment and household durables would fix it. Adding government consumption shocks – which in their specification are temporary – cuts persistence rather than raising it, because with no investment margin available hours must rise sharply to absorb the shock; it also reduces the excess volatility of consumption, and contributes almost nothing to output volatility. They describe the work as “primarily as preliminary and, we hope, suggestive.”

Richard M. Todd | Macro Paper Warehouse

Time to Plan and Aggregate Fluctuations