Estimating Learning Curves from Aggregate Monthly Data

Abstract

In this paper the problems of using aggregate monthly data to estimate learning curves are investigated. Here, aggregate monthly data on labor hours are assumed to contain some of both fixed and variable labor hours. They are also assumed to be influenced by fluctuating quantities of work in process. A distributed lag model is developed to deal with these two characteristics of aggregate monthly data. The model is generalized to permit production rate to influence labor productivity. This generalized model is then estimated and compared to a cumulative average learning curve in analyzing the impact of a production break. A set of production data which arose from a government contract claim is used for this purpose.

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