2010
DOI: 10.1093/icc/dtq051
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Technological regimes, Schumpeterian patterns of innovation and firm-level productivity growth

Abstract: The paper investigates the relationships between technological regimes and firm-level productivity performance, and it explores how such a relationship differs in different Schumpeterian patterns of innovation. The analysis makes use of a rich dataset containing data on innovation and other economic characteristics of a large representative sample of Norwegian firms in manufacturing and service industries for the period 1998-2004. First, we decompose TFP growth into technical progress and efficiency changes by… Show more

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Cited by 76 publications
(26 citation statements)
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“…We have also included dummies for the Italian regions firms belong to in order to control for an investment localization effect: 18 it is reasonable to believe that 15 See Arellano and Bond (1991). 16 In the definition of the Schumpeterian patterns, in terms of sectors of economy they include, we have followed Castellacci and Zheng (2010). When a sector included in our dataset does not appear in the Castellacci and Zheng classification, we adopted a dimensional criterion: small and medium size firms are included in SMI, large firms are included in SMII.…”
Section: Persistence Estimation Resultsmentioning
confidence: 99%
“…We have also included dummies for the Italian regions firms belong to in order to control for an investment localization effect: 18 it is reasonable to believe that 15 See Arellano and Bond (1991). 16 In the definition of the Schumpeterian patterns, in terms of sectors of economy they include, we have followed Castellacci and Zheng (2010). When a sector included in our dataset does not appear in the Castellacci and Zheng classification, we adopted a dimensional criterion: small and medium size firms are included in SMI, large firms are included in SMII.…”
Section: Persistence Estimation Resultsmentioning
confidence: 99%
“…As pointed out above, the level of technological opportunities in a given industry (the parameter γ j ) is an industry-specific factor that varies substantially across sectors (Dosi, 1982;Castellacci and Zheng, 2010). Opportunities are typically high in technologically advanced and new emerging sectors, and lower in more traditional and mature industries (Von Tunzelmann and Acha, 2005).…”
Section: A Framework For Future Researchmentioning
confidence: 99%
“…The increasing availability and popularity of this useful data source has been one of the main driving forces behind the development of the CDM model (e.g. Crepon et al, 1998;Hall and Mairesse, 2006;Griffith et al, 2006), since CIS data make it possible to measure both the inputs and outputs of the innovative process, and to analyse their effects on the economic performance of enterprises (Castellacci and Zheng, 2008).…”
Section: Data Indicators and Descriptive Evidencementioning
confidence: 99%