This paper shows that the available stylized facts on productivity dynamics, such aspersistent cross-sectoral heterogeneity, do not allow to solve an identification problemregarding the impact of common drivers - such as General Purpose Technologies (GPTs) - oneconomic growth. The evidence of persistently heterogeneous productivity performances isconsistent both with a GPT-driven model, and with a model characterized by purelyindependent and idiosyncratic sectoral dynamics. These results are obtained within a simpletheoretical framework, and illustrated with reference to measures of concentration of thesectoral contributions to aggregate total factor productivity growth.

Modelling Smooth and Uneven Cross-Sectoral Growth Patterns: an Identification Problem

SAPIO, Alessandro
2006

Abstract

This paper shows that the available stylized facts on productivity dynamics, such aspersistent cross-sectoral heterogeneity, do not allow to solve an identification problemregarding the impact of common drivers - such as General Purpose Technologies (GPTs) - oneconomic growth. The evidence of persistently heterogeneous productivity performances isconsistent both with a GPT-driven model, and with a model characterized by purelyindependent and idiosyncratic sectoral dynamics. These results are obtained within a simpletheoretical framework, and illustrated with reference to measures of concentration of thesectoral contributions to aggregate total factor productivity growth.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/28914
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