Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity

LeSage, James P.; Fischer, Manfréd M.

doi:10.1177/0160017611407767

Cited by 37 publications

(36 citation statements)

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“…This is an appropriate way to deal with the problem of spatial autocorrelation, and to estimate the influence of spatial spillover effects. The panel version of the standard SDM model controls not only for spatial autocorrelation but also for individual heterogeneity across regions (see LeSage and Fischer 2012). Denoting our set of regions by i = 1, ..., N and our time periods by t = 1, ..., T, the empirical model to estimate the relationship between research collaboration and regional knowledge production is given by …”

Section: The Empirical Modelmentioning

confidence: 99%

Research Collaboration and Regional Knowledge Production in Europe

Hidas

Wolska

Fischer

et al. 2013

The Geography of Networks and R&D Collaborations

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract. The focus of this study is on regional knowledge production in Europe, with special emphasis on the interplay between intra-and inter-regional research collaboration. The objective is to identify and measure effects of research collaboration on knowledge production at the level of European regions. We use a panel data spatial Durbin model (SDM) relationship for empirical testing. The European coverage is achieved using 228 NUTS-2 regions covering all pre-2007 EU member states except Cyprus, Greece and Malta. The dependent variable, regional knowledge production, is measured in terms of fractional patent counts at the regional level in the time period [2000][2001][2002][2003][2004][2005][2006][2007][2008], using patents applied at the European Patent Office (EPO). The independent variables include an agglomeration variable, reflecting intra-regional research collaboration, measured in terms of employment in knowledge intensive sectors, and a network variable, reflecting extra-regional research collaboration, measured in terms of a region´s collaboration activities in the EU Framework programmes (FPs), weighted by R&D expenditures in network partner regions. We implement a panel version of the standard SDM that controls for spatial autocorrelation as well as individual heterogeneity across regions, and allows for the estimation of spatial spillovers from neighbouring regions. The estimation results confirm the prevalence of agglomeration effects for regional knowledge production, and, by this, the importance of co-location of R&D actors. Furthermore, the study provides evidence that inter-regional R&D collaborations in the FPs significantly contribute to regional knowledge production. Up to now, there is only little empirical evidence on the local-global duality in knowledge creation at the regional level. In this study we take a regional perspective to address this question drawing on novel data sets providing information on project based networking activities in the The paper is organised as follows. Section 2 sheds some light on the theoretical background for the study, focusing on regional knowledge production and the importance of extra-regional research collaboration for gaining access to external knowledge sources. Section 3 outlines the econometric framework, specifying the empirical model in form of a panel version of the SDM relationship to be estimated. Section 4 comprises a detailed description of the empirical setting, presenting the ...

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Section: The Empirical Modelmentioning

confidence: 99%

Research Collaboration and Regional Knowledge Production in Europe

Hidas

Wolska

Fischer

et al. 2013

The Geography of Networks and R&D Collaborations

Self Cite

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“…MAR knowledge externalities will have obtained the results with industrial maturity. Lesage and Fischer [29] tested the existence of knowledge externalities with variable total factor productivity. The scope of the study data was used in 15 EU countries.…”

Section: Literature Review Data and Methodologymentioning

confidence: 99%

Knowledge Externalities: An Application to the Turkish Manufacturing Industry

Daştan¹,

Güneş²,

Çalmaşur³

2015

aeb

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In the sequel of technological breakthroughs as advanced in recent years, the notion of mileage have started to eradicate on Earth. Created knowledge and innovations disseminate along distinct people or institutions promptly. Fast paced technological advancements of communication and attaining have brought countries closer so as to transform the world to a, so to say, "global village". In this new world's order, where economical and social circumscriptions fade out gradually, the economy shall be undertaken as an entirety as circumscriptions are being glossed over. Knowledge and innovations of a company which the same are created by disseminate promptly in the new world's order which is being unfold, and introduce positive influences to the other companies and industries. The influences, here concerned are, observed on the closely positioned companies and industries along with the ones so remotely positioned to each other. In this study, the presence of a limited number of studies on externalities taking place in the Turkish manufacturing industry, the importance of externalities, will be a related topic aimed to generate resources for research. In line with this idea and with consider to NACE Rev.2 classification, knowledge externalities that arise in Turkish manufacturing industry are analyzed for the entirety of the industry and for each sub-sector distinctively within the 2003-2013 years of period. With regard to the test results of GMM estimator, where the tests were conducted separately for each of the sub-sectors of manufacturing industry; MAR, Jacobs, Porter, and III variable might have both positive and negative influences. MAR knowledge externalities (resulting from specialization) were probed to be meaningful in 9 sectors; whereas Jacobs's (resulting from diversity) in 7 sectors, and Porter's (resulting from competition) in 6 sub-sectors of the manufacturing industry. Also, III variable (resulting from innovation) was proved to be meaningful for the 9 sub-sectors of the manufacturing industry.

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“…Most of these studies on knowledge externalities present empirical results for European countries. Lesage and Fischer(2012), Autant-Bernard and LeSage(2011), Antonelli et al(2011), Zheng(2010, Fischer et al(2009), Frenken et al(2007, Suedekum and Blien(2005), Van Stel andNieuwenhuijsen(2004), De Lucio et al(2002) and Paci and Usai(1999) are the studies that have found mainly positive impacts of knowledge externalities in the European economies. However Neffke et al(2011) applied an empirical analysis for Sweden during 1974 -2004 and they found that as the maturity of an industry increases the impacts of Jacobs externalities may turn to negative.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Especially in recent years, numerous studies in knowledge externalities literature such as LeSage and Fischer(2012), Antonelli et al(2011), Zheng(2010 and Fischer et al(2009) used TFP as a dependent variable. In this study, regional TFP is also used as the dependent variable which reflects economic progress and growth of a region.…”

Section: Variables and Econometric Modelmentioning

confidence: 99%

Knowledge Externalities in Turkish High-Technology Manufacturing Industries: Spatial Panel Data Analyses

Türkcan

2015

Journal of Yaşar University

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Knowledge externalities are defined as positive spillover outcomes of new and innovative knowledge among firms. They create positive impacts on firms' productions, which did not participate in the production process and the costs of new knowledge creation. Knowledge externalities have critical importance especially for regional economies due to the fact that firms locating in the same region create networks in which knowledge disseminates quickly. This paper presents empirical evidences about the existence, types and impacts of knowledge externalities in Turkish regions for 1989 -2008 time period. The main focus of this study is medium-high and high-technology industries due to the fact that these industries produce innovative outcomes and hence affect regional economies by creating knowledge externalities. In this context empirical evidences indicate that dynamic knowledge externalities occur more frequently than static ones and in general they affect regional economies positively in Turkey. Also, MarshallArrow-Romer knowledge externalities most frequently occurred before 2001 while after this year, Porter knowledge externalities have most frequently occurred in medium-high and high technology industries in Turkish regions. Keywords: Knowledge Externalities, High Technology Industries, Spatial EconometricsÖz: Bilgi dışsallıkları, firmalar arasındaki yeni ve yenilikçi bilginin pozitif yayılma çıktıları olarak tanımlanmaktadır. Bilgi dışsallıkları, yenilikçi bilginin üretim sürecine ve dolayısıyla da yeni bilgi yaratımının maliyetine ortak olmayan firmalar üzerinde pozitif etkiler yaratmaktadır. Belli bir mekansal alanda odaklanmış firmalar arasındaki ağ ekonomilerinin bilgi akışını hızlandırması nedeniyle ise bilgi dışsallıkları, özellikle bölgesel ekonomiler için kritik öneme sahip olmaktadır. Bu çalışma, Türkiye'de bölgeler itibariyle 1989 -2008 zaman periyodunda bilgi dışsallıklarının varlığını, ortaya çıkan bilgi dışsallıklarının türlerini ve ekonomik etkilerini ampirik bulgular ile ortaya koymaktadır. Bu çalışmanın temel odak noktası, yenilikçi çıktılar üretmeleri ve dolayısıyla bilgi dışsallıkları üreterek bölgesel ekonomileri etkilemeleri açısından orta-yüksek ve yüksek teknolojili endüstrilerdir. Bu bağlamda ampirik bulgular, Türkiye'de dinamik bilgi dışsallıklarının static bilgi dışsallıklarından daha sık ortaya çıktığını ve bölgesel ekonomileri de genellikle pozitif etkilediklerini göstermektedir. Ayrıca, Marshall -Arrow -Romer bilgi dışsallıklarının 2001 yılı öncesinde bölgelerde en sık ortaya çıkan bilgi dışsallığı türü olduğu gözlenirken, 2001 sonrasında Porter bilgi dışsallıklarının en sık görülen bilgi dışsallıkları haline geldiği tespit edilmiştir.

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Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity

Cited by 37 publications

References 51 publications

Research Collaboration and Regional Knowledge Production in Europe

Research Collaboration and Regional Knowledge Production in Europe

Knowledge Externalities: An Application to the Turkish Manufacturing Industry

Knowledge Externalities in Turkish High-Technology Manufacturing Industries: Spatial Panel Data Analyses

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