Abstract
In this paper quantile regression is used to explore the relation of current and lagged productivity levels of U.S. manufacturing industries. Productivity is calculated in the form of total factor productivity by a non-parametric approach. Bootstrap-based confidence intervals and specification tests are reported. Key findings are that a first-order Markov process provides a valid description of productivity transitions and that persistence is larger in the range of higher productivity levels. This is reinforced by the widespread insignificance of additional conditioning variables. A notable exception is the variability of the growth path which increases explanatory power substantially.
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