Geographical Clustering and the Effectiveness of Public Innovation Programs

Abstract

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. December 2016 Geographical Clustering and the Effectiveness of Public Innovation Programs AbstractThe paper analyzes how geographical clustering of beneficiaries might affect the effectiveness of public innovation support programs. The geographical proximity of firms operating in the same industry or field of technology is expected to facilitate innovation through knowledge spillovers and other localization advantages. Public innovation support programs may leverage these advantages by focusing on firms that operate in a cluster. We investigate this link using data from a large German program that co-funds R&D projects of SMEs in key technology areas called 'Innovative SMEs'. We employ three alternative cluster measures which capture industry, technology and knowledge dimensions of clusters. Regardless of the measure, firms located in a geographical cluster are more likely to participate in the program. Firms being part of a knowledge-based cluster significantly increases their chance of receiving public financial support. We find no effects, however, of geographical clustering on the program's effectiveness in terms of input or output additionality.JEL-Classification: C35, H50, O31, O32, O38, R59

Why are there so few hard facts about the impact of cluster policies in Germany? A critical review of evaluation studies

“…Empirical findings were mirrored against expectations regarding desirable influences of cluster support on the development of clusters, their actors and their economic environment, which can be found in the theoretical cluster literature and have often been incorporated into programme documents on cluster support. Important sources in this context are Alecke et al 2008;Andersson et al 2004;Audretsch et al 2016;Brenner et al (2013); Brökel and Graf 2020;Crass et al 2016;Eickelpasch et al 2004;Falck et al 2011;Ketels 2014;Porter 1998Porter , 1999Santner 2018;Segers 2016;Sölvell and Williams 2013;Uyarra and Ramlogan 2016;Vicente 2016 of R&D projects, depends heavily on the level of risk connected with the respective projects.…”

Special issue: clustering and innovation: firm-level strategizing and policy

“…Several authors have stressed the importance of learning processes, networks and inter-firm cooperation in cluster performance (Harhoff, Henkel, and von Hippel 2003;Saxenian 1991). The stimulus for innovation is derived from certain characteristics of the cluster, especially in relation to the structure and combination of knowledge flows (Audretsch and Feldman 2004;Cooke 2001;Crass, Rammer, and Aschhoff 2016). Information and communication technology has led to a decrease in the cost of transferring information over long distances.…”