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 amount of 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 both on aggregate 2-digit data 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.