The innovation efficiency of German regions – a shared-input DEA approach

Abstract

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. Institute of Economic and Cultural Geography, Leibnitz University, Hannover. Terms of use: Documents in Nicky RoggeFaculty of Business and Economics Naamsestraat 69, Katholieke Universiteit Leuven. Thomas BrennerEconomic Geography and Location Research, Philipps-University, Marburg. Abstract:The paper contributes to the debate on how to measure regions' innovation performance. On the basis of the concept of regional innovation efficiency, we propose a new measure that eases the issue of choosing between industry-specific or global measures. We argue for the use of a robust shared-input DEA-model to estimate regions' innovation efficiency in a global manner, while it can be disaggregated into industry-specific innovation efficiency measures. The latter is particularly useful when relating the innovative output to the R&D input involves the use of blurry matching procedures.We illustrate the use of the method by investigating the innovation efficiency as well as its change in time of German labor market regions. It is shown that the method treats regions that have industry structures skewed towards industries with high and low innovation intensities more fairly than traditional approaches.

A multilevel and multistage efficiency evaluation of innovation systems: A multiobjective DEA approach

“…The previous regional efficiency estimations are justified by the relevant literature. For example, Broekel, Rogge, and Brenner (2013) used a shared input DEA model to estimate the innovation efficiency of German regions. They found that the mean efficiency is 0.709 in 20 04-20 08, which is comparable with the mean value of German regional efficiencies in this study (0.750).…”

Production of output and ideas: efficiency and growth patterns in the United States

“…Rich and detailed data on german regions have allowed the study of regional innovation performance and the evaluation of various policy measures. 28 The studies of Broekel and Schlump (2009), Brenner and Broekel (2011), Fritsch and Slavtchevc (2011), Broekel (2012 and Broekel et al (2013), in particular, have studied the efficiency of regional (and industry) specific innovativeness and the role of regional factors, i.e., knowledge networkings and collaboration among regional organizations using (mainly non-parametric) frontier methodologies in measuring innovation efficiency. For example, Fritsch and Slavtchevc (2011) estimate a knowledge production function to assess innovation efficiency for 97 planning german regions, documenting considerable differences in technical efficiency across regions, which are specially divided into different regimes with divergent level of performance.…”

“…While cross-country efficiency analyses have their relevance, the study of efficiency performance at regional level offers valuable insights. Recently an emerging line of research performs efficiency analysis at regional level with the vast majority to focus on innovation efficiency in Germany (Broekel and Schlump, 2009;Brenner and Broekel, 2011;Fritsch and Slavtchevc, 2011;Broekel, 2012;Broekel et al, 2013) and China (Altvater-2 See, for instance, Jones (1995), Coe and Helpman (1995), Aghion and Howitt (1998), Griffith et al (2004a), Zachariadis (2003), Bottazzi and Peri (2003), Bottazzi and Peri (2007), and Mancusi (2008) among others. 3 Various studies also investigate the role of efficiency in explaining growth differentials for a panel of manufacturing industries in the OECD countries.…”

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

“…Wang et al [9] explored the environmental components of regional innovation efficiency in China, including economic infrastructure, the quality and structure of innovators, and regional openness, and found a chain structure relationship between regional innovation environmental components and innovation efficiency. Broekel, Rogge, and Brenner [7] proposed a robust shared-input DEA model to compute regions' innovation efficiency, and found a considerable variance in regional innovation efficiencies among German regions. Li et al [5] observed a considerable regional variation in innovation efficiency in China through DEA estimates, and found a positive effect of foreign direct investment on regional innovation efficiency.…”

“…A country's technological innovation activities always have obvious regional characteristics [1][2][3][4], and the efficiency of regional innovation can differ among regions [5][6][7][8]. Technological innovation can be seen as a main driver of regional development and economic growth [9], but at the same time can also bring sincere damage to the natural environment when it is unsustainable [10].…”