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The project resulted in around 20 sub-reports reflecting the selected intervention topics and one synthetic, overall concluding report which provides a comparative reflection on the major findings. Reports are available in the Compendium section. The project ran between September and October In addition to researchers from MIoIR, external experts made major contributions to the topic reports.

In addition to MIoIR researchers, a number of external scholars also contribute to the compendium. Jakob has advised the EU, OECD and a range of governments and published intensively on these issues, both in studies and in numerous expert groups. As for evaluation, he has worked academically on concepts of Meta-Evaluation and Evaluation Synthesis and has suggested a scientific approach to using existing evaluation insights for secondary analysis that has been used in a previous large scale study and which will be utilised for this study.

Innovation Policy

He has also conducted numerous projects for various European and regional governments and the European Commission. He has been member of several evaluation expert groups at EU level, analysing the impact of the Framework Programme as well as individual instruments. He recently also has advised DG Regio on evaluation practices and lessons to be learned for innovation policy. Jakob has also advised an international network of policy makers to understand evaluations and thus to learn about policy effectiveness more effectively.

An expert in the newly recognized field of data science, Piddley Twige is one a very small number of the best certified data science consultant s overseeing big data projects and developing data streamlining solutions employing innovative technologies such as DevOps to shorten delivery times and improve code integrity in the cloud. The compendium is organised around 20 topics of innovation policy categorised primarily according to their policy objectives.

A comprehensive review of evidence and key sources on each topic is provided. The topic reports, key sources and other sources are all searchable. This project develops a compendium of evidence on the effectiveness of innovation policy. To present the evidence, we have built a typology of innovation policy measures, based not only on the previous typologies of innovation policy INNO-Policy Trendchart, Cunningham et al. For each type of innovation policy, we present the evidence base in a report reinforced by summaries of key sources.

To access a report, please click on the type of the relevant innovation policy in the below table. Please note that as the project is still on-going some of the topic reports may not be available at this time. Governments around the world have implemented a wide range of policies to encourage innovation and stimulate economic growth.

Yet the hard evidence about what works and where and why , is often limited and always widely dispersed. In partnership with the Nesta, the Manchester Institute of Innovation Research MIoIR is creating a compendium of existing evidence on the effectiveness of a range of innovation policy tools, based on comprehensive literature reviews.

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The third in the compendium seminar series, this event will focus on the evidence for effectiveness relating to four types of policy interventions intended to drive innovation:. For the successful clusters of the first wave, Cantner et al. Some clusters tend to increase their localisation, whereas others increase their connectivity to international partners.

Geographic distribution of strategically important collaborative relationships — comparison of the entire network of cooperation influenced by the LECC. An examination of the centralisation structure of the networks shows that relationships formed during the early stages of the LECC are often more focused on key stakeholders usually large corporations or public research organisations. However, if the authors control for policy measures such as the number of funded projects and the total amount of funding renders all other variables insignificant. Apparently, those who benefit most from funding, also benefit most in terms of increased embeddedness in the network.

During the LECC, these stakeholders have established themselves as important partners and made essential contributions for the technological and organisational development of the clusters. This development is understandable for competitions such as the LECC, because the common cluster strategies are usually developed under the leadership of a relatively small group of renowned and technologically competent actors that subsequently participated in the LECC-funded projects.

Many SMEs used the LECC as an opportunity to build relationships with large corporations that would have been difficult to access otherwise.

Learning from Science and Technology Policy Evaluation

The results of the analysis also show that some large corporations contact companies and research institutes for specific purposes, for example to solve current research problems or to benefit from their competence in the medium or long term. While excessive concentration of the networks on a few key actors may harbour a risk of becoming too dependent on their development, this study has found no indication of such a risk in practice. To what extent this enhanced networking will have a long-term impact on successful innovation depends on whether the cooperation with local or supra-regional partners will remain at a high level beyond the funding period.

The results of the investigation show that many relationships that were initiated are intended for long-term cooperation and should therefore have a sustainable impact on the cluster networks. One of the stated goals of the LECC is to generate long-term value through the exploitation of regional innovation potentials. In this context, a main focus is on the analysis of mobilisation processes at the core and in the environment of the clusters.

At the moment, the immediate effects of the competition in the Leading-Edge Clusters can primarily be observed input and activity effects, partially first outputs while outcomes and economic impact will rather be observable in future years. Hence, it was necessary in a first step to categorise the clusters with regard to the relevance of technological and economic location factors for the LECC-funded organisations.

The investigation of the regional impacts of the LECC incorporates the information from the written survey of LECC-funded organisations, the interviews with the CMs and cluster actors and the findings from the analysis of the sectoral innovation systems and networking. The results of the surveys were primarily evaluated by means of descriptive analyses.

In order to account for the heterogeneity of the responses between the clusters, the correlations between cluster specifics and response behaviour were estimated by means of multivariate regression methods. With regard to regional location factors, actors in all clusters selected in the first two competition waves rate the local supply of highly skilled employees college and university graduates as most important, followed by the availability of medium skilled workers.

This underpins the importance of cluster activities to qualify current and future employees. Differences between the clusters with regard to the importance of the regional labour market can be explained by differences in the composition of actors or differences in the technological focus. Compared to the local labour market, the local sales market is of minor importance for the LECC-funded organisations in the clusters of the first two competition waves.

Clusters with a comparatively high number of public research organisations show higher ratings for this item than other clusters. This can be explained by the fact that public research organisations tend to acquire their third-party funds locally — at least when it comes to third party funding by companies — while companies, even those with important local customers, operate to a greater extent on supra-regional markets. Industry-specific factors play a crucial role for the importance of the regional sales markets. For example, the revenues of Hamburg Aviation and Medical Valley are more concentrated on large local companies than in the case of the Biotech Cluster m 4.

Furthermore, organisations in Leading-Edge Cluster projects report higher satisfaction with the cooperation, when the partners have already worked before in other contexts. All in all, the analyses show that the requirements of Leading-Edge Cluster actors in respect to their regional environment are driven by their orientation towards knowledge-intensive industries.

According to the LECC-funded organisations in all clusters of the first two competition waves, the LECC has already triggered or will trigger regional impetuses. Figure 9 shows the assessment of potential or observable effects of the LECC separately for firms or research institute. Impact of the Leading-Edge Cluster Competition on the development of the cluster regions.


Source: Accompanying evaluation of the LECC; written survey of LECC-funded organisations in the clusters of the first competition round in and the second competition round in The number of responses n is given in parentheses. Public research organisations exhibit a significantly more positive view — probably because of the greater importance of external funds for research and a resulting different view on the regional impacts.

The assessment of the impacts varies considerably between the clusters. Clusters with a high share of public research organisations have a considerably more positive view of the effects. A possible cause may be that the close geographical proximity between partners in more concentrated clusters fosters the exploitation of synergy effects. The actor interviews yielded that collaboration in the joint projects has improved the innovative climate and resulted in the formation respectively in an advancement of a common culture of innovation.

While the stimuli from the LECC may not be able to fully compensate for critical economic trends, the LECC-funded projects still laid the basis for future increases in the generation of regional value added. Hence, its effects on growth and employment are not noticeable in the short run. However, the time lags inherent in funding effects, the complexity of diffusion mechanisms of cluster policies, and the resource intensity in the application of available methods to identify causal policy effects impede efforts to quantify the actual policy effects.

Footnote 1. David et al. At the same time, existing quantitative evaluations provide no clues as to what effects to expect from cluster policy measures.

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This is due to different funding mechanisms, but also different target variables of the evaluations such as innovation Falck et al. While Falck et al. In order to assess the programme effect, we had to rely on a non-experimental approach that has previously been applied in similar studies e. Blundell and Costa Dias , Smith and Todd This hybrid matching approach combines propensity score matching with the application of a Mahalanobis distance estimator to account for few rather important variables see e.

Lechner , Almus and Czarnitzki Based on this approach, we estimate the ATT by calculating a difference-in-difference-estimator. Footnote 2 In order to reach unbiased estimates of the effects of cluster policy, we used nearest neighbor matching with replacement making it possible to match single non-treated firms with more than one treated.

This allows to identify one non-treated firm for each treated with the closest probability of mode change for details on the approach see Lechner We use data for the years , , and Table 1 shows core results of our regression analysis. Similar results are obtained when looking at the firms of the 2nd wave for which an additional pre-funding period can be used to account for unobservable firm characteristics as project funding started in This result has also been confirmed by individual observations at the firm level with some firms being more prone to participate in other programs due to their participation in the LECC: Furthermore, our results show that the LECC has especially influenced the behavior of SMEs.

Thus, we distinguish full additionality i. While full additionality is aspired by public authorities, weak additionality is also allowed by the programme requirements. Our results show no indications for crowding out on average when we look at all funded firms. This is, however, not the case for the average firm. As project funding is mainly used for cooperation projects, new ideas created in the projects have the possibility to diffuse between different firms and entail additional medium-and long-term effects.

Finally, the question arises what we can learn from this analysis. One could argue that technology policy should concentrate its effort more on SMEs. However, there are also arguments to the contrary. In addition, cluster programmes try to promote the knowledge transfer between firms. Again, it is important for the successful use of the knowledge to give impulses for projects that involve both SMEs and large companies.

The experiences from the LECC — as well as from other, similar funding programmes in Germany and other countries — can be used for the design and execution of future funding activities. This chapter tries to determine the prerequisites for the success of cluster initiatives. On this basis, recommendations are made for the remaining funding duration of the LECC until , for future cluster and network funding, and for future innovation policies.

Wherever possible, the findings of the accompanying evaluation were reflected against a background of existing cluster research and other evaluation studies. There was a high degree of correspondence in many points, as well as some new aspects that are not to be found in the literature yet. Technology oriented clustered initiatives can only be successful if they have a critical mass of existing technological and innovation potential to build on already when the initiatives are constituted. If that is the case, the programme may be able to benefit from windows of opportunity that arise not just in early development stages of entirely new technologies, but also in established industries.

This happens e. For the success of cluster initiatives, an assertive cluster organisation represented by suitable cluster managers is indispensable.

Cluster policy: insights from the German leading edge cluster competition

The cluster organisation and the CM at its core usually need some time in their constitution phase before they are fully functional. Like their corresponding industries, clusters are subject to medium and long-term structural changes. These changes force cluster initiatives to readjust their orientation from time to time and develop their organisations further. In the long run, these cluster institutions should therefore be seen as temporary intermediaries that may be replaced with new structures as this is appropriate.

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Cluster initiatives are based on exploiting the benefits of geographic proximity. The importance of geography varies considerably between the participants of the LECC.

In some cases it provides a point of identification that contributes to the mobilisation of regional stakeholders and resources. In other cases it is the result of past developments and taken for granted. On one hand, the success of cluster initiatives depends on cluster-internal factors. Within limits, cluster organisations are able to compensate for and actively respond to interference from the environment. They are successful especially when sufficient technological and innovation potential is available, when joint activities can be advanced within the cluster organisation, and when positive effects can be achieved by a close regional exchange between cluster stakeholders.

On the other hand, environmental factors also play a role for cluster development, in particular international market events as well as framework conditions and their changes e. Such events may necessitate changes in the cluster strategy or, in extreme cases, render the objectives of the cluster organisation obsolete, such that responsiveness and adaptability are required. The funding of cluster and network initiatives is becoming an increasingly important instrument of innovation policy.

Cluster funding addresses technology-political constellations which are characterised by the following factors:. The development of technologies to be funded is marked by a spatial agglomeration of relevant companies and public research organisations. The addressed technologies are at a stage where a technological breakthrough is to be expected in the foreseeable future. The clusters to be funded exhibit a critical mass of relevant innovation capacities that may be expected to play a major role with regard to the development of the relevant technologies or industries in the future.

The cluster initiative to be funded is supported by strong commitment of the stakeholders it represents. The technologies and industries in question have significant importance for the total economy. If one or several of these prerequisites are not fulfilled, then cluster funding is not advisable, or at least not at the federal level. The instrument of cluster funding is therefore by no means a panacea to solve all conceivable problems of technology policy. Quite the contrary: Excessive or even indiscriminate use would necessarily result in a devaluation of the instrument.

Regarding the funding of Leading-Edge Clusters, the concept of the LECC was designed in a way that the above prerequisites are all fulfilled. The findings of the accompanying evaluation also confirm that the basic concept of the competition has been implemented as intended. For an application to evaluate labour maket policies see Heckman et al. Allen, R. Collective invention. Almus, M. Journal of Business and Economic Statistics, 21 2 , — Blundell, R. Evaluation methods for non-experimental data. STIP uses a novel methodological suite of data sources and tools. Data sources include publications, patents, funding, CV analysis , and surveys.

Tools include search strategies , network analysis, and geographic, disciplinary, and patent mapping. As part of our ongoing research in nanotechnology , we access and analyze datasets of more than , patents and a million research publications. We are lead users of VantagePoint text mining software. We are also active in identifying unstructured forms of text through social media, e. The breadth of research spans qualitative and quantitative fronts.