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
2006-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
