Learning by Doing and Aggregate Fluctuations

A major unresolved issue in business cycle theory is the construction of an endogenous propagation mechanism capable of capturing the persistence displayed in the data. In this paper we explore the quantitative implications of one propagation mechanism: learning by doing. Estimation of the parameters characterizing learning by doing is based on aggregate, 2-digit and plant level observations in the US. The estimated learning by doing function is then integrated into a stochastic growth model in which fluctuations are driven by technology shocks. We conclude that learning by doing can be a powerful mechanism for generating endogenous persistence. Moreover learning by doing modifies the labor supply decision of the representative agent making it forward looking. This has a number of implications for the interpretation of labor supply shifts as “taste shocks” and the cyclical utilization of labor which we explore in the paper. Correspondence to: Professor Alok Johri Department of Economics McMaster University 1280 Main Street West Hamilton, Ontario Canada L8S 4M4 E-mail: [email protected] Cooper-Johri [1997] investigates the role of dynamic complementarities in the production function for the propagation 1 of temporary shocks to productivity and tastes and find the propagation effects can be quite strong : an iid technology shock creates serial correlation in output of about .95 using their estimated complementarities. In the present paper these complementarities are ignored in order to isolate the effects of internal learning by doing. 1 Learning by Doing and Aggregate Fluctuations I. Motivation One of the major unresolved issues in business cycle theory is the construction of an endogenous propagation mechanism capable of capturing the amount of persistence observed in the data. The fact that the standard real business cycle (RBC) model has a weak internal propagation mechanism is evident from early work on these models, such as King, Plosser and Rebelo [1988]. This point has been made again in recent papers by Cogley and Nason [1995] and Rotemberg-Woodford [1996] in the context of output growth: the data indicate that U.S. output growth is positively autocorrelated in contrast to the predictions of the standard RBC model. In this paper we study the role of internal learning by doing in propagating shocks. This is done by supplementing an otherwise standard representative agent stochastic growth model by introducing internal learning by doing. As we shall see, this creates a new state variable, which we label 1 organizational capital, that reflects past levels of activity at the plant or firm. The idea is simply to capture the fact that the production process creates information about the organization of the production facility that improves productivity in the future. As discussed at some length in Section II, there is abundant empirical support for learning by doing. We see our notion of organizational capital as including the accumulation of experience by workers and managers but also, as in Benkard [1997], allowing for depreciation of this capital as well.